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     "text": [
      "Starting Spark application\n"
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       "<table>\n",
       "<tr><th>ID</th><th>YARN Application ID</th><th>Kind</th><th>State</th><th>Spark UI</th><th>Driver log</th><th>Current session?</th></tr><tr><td>14</td><td>application_1584073442397_0015</td><td>pyspark</td><td>idle</td><td><a target=\"_blank\" href=\"http://ip-172-31-29-161.ap-northeast-1.compute.internal:20888/proxy/application_1584073442397_0015/\">Link</a></td><td><a target=\"_blank\" href=\"http://ip-172-31-29-161.ap-northeast-1.compute.internal:8042/node/containerlogs/container_1584073442397_0015_01_000001/livy\">Link</a></td><td>✔</td></tr></table>"
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     "name": "stdout",
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     "text": [
      "SparkSession available as 'spark'.\n"
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     "text": [
      "<SparkContext master=yarn appName=livy-session-14>"
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    "sc"
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   ],
   "source": [
    "from pyspark.sql.types import *\n",
    "from pyspark.sql import SparkSession\n",
    "import pyspark.sql.functions as f\n",
    "import warnings; warnings.simplefilter('ignore')\n",
    "import datetime\n",
    "\n",
    "# config spark to build context\n",
    "spark=SparkSession.builder.appName(\"NYC.TLC-Green-Car-Data-ETL\").getOrCreate()\n",
    "spark.conf.set(\"spark.sql.session.timeZone\", \"America/New_York\")\n",
    "\n",
    "#read in our raw dataset\n",
    "df = spark.read.option('header', 'true') \\\n",
    "    .option('mode', 'FAILFAST') \\\n",
    "    .option('timeStampFormat', 'yyyy-MM-dd HH:mm:ss') \\\n",
    "    .option('columnNameOfCorruptRecord', 'error') \\\n",
    "    .csv('s3://data-etl-o-original-raw/green/*.csv') \\\n",
    "    .filter(f.year('lpep_pickup_datetime') == 2019) \\\n",
    "    .withColumn('duration', f.unix_timestamp('lpep_dropoff_datetime') - f.unix_timestamp('lpep_pickup_datetime')) \\\n",
    "    .withColumn('minute_rate',f.when(f.col('total_amount') <= 0.001,0).otherwise(f.col('total_amount')/f.col('duration') * 60)) \\\n",
    "    .withColumn('average_speed',f.when(f.col('trip_distance') < 0.001,0).otherwise(f.col('trip_distance')/f.col('duration') * 60 * 60)) \\\n",
    "    .drop('store_and_fwd_flag') \\\n",
    "    .drop('ehail_fee') \\\n",
    "    .drop('congestion_surcharge')\n"
   ]
  },
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   "execution_count": 8,
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    {
     "data": {
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       "model_id": "8a29412dd9fa42d695fdd4d5ca1faa2d",
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       "VBox()"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame[VendorID: string, lpep_pickup_datetime: string, lpep_dropoff_datetime: string, RatecodeID: string, PULocationID: string, DOLocationID: string, passenger_count: string, trip_distance: string, fare_amount: string, extra: string, mta_tax: string, tip_amount: string, tolls_amount: string, improvement_surcharge: string, total_amount: string, payment_type: string, trip_type: string, duration: bigint, minute_rate: double, average_speed: double]"
     ]
    }
   ],
   "source": [
    "dfcache = df.cache()\n",
    "\n",
    "dfcache"
   ]
  },
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   "execution_count": 86,
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    {
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     "text": [
      "root\n",
      " |-- VendorID: string (nullable = true)\n",
      " |-- lpep_pickup_datetime: string (nullable = true)\n",
      " |-- lpep_dropoff_datetime: string (nullable = true)\n",
      " |-- RatecodeID: string (nullable = true)\n",
      " |-- PULocationID: string (nullable = true)\n",
      " |-- DOLocationID: string (nullable = true)\n",
      " |-- passenger_count: string (nullable = true)\n",
      " |-- trip_distance: string (nullable = true)\n",
      " |-- fare_amount: string (nullable = true)\n",
      " |-- extra: string (nullable = true)\n",
      " |-- mta_tax: string (nullable = true)\n",
      " |-- tip_amount: string (nullable = true)\n",
      " |-- tolls_amount: string (nullable = true)\n",
      " |-- improvement_surcharge: string (nullable = true)\n",
      " |-- total_amount: string (nullable = true)\n",
      " |-- payment_type: string (nullable = true)\n",
      " |-- trip_type: string (nullable = true)\n",
      " |-- duration: long (nullable = true)\n",
      " |-- minute_rate: double (nullable = true)\n",
      " |-- average_speed: double (nullable = true)"
     ]
    }
   ],
   "source": [
    "## check schema structure\n",
    "df.printSchema()"
   ]
  },
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     "text": [
      "+--------+--------------------+---------------------+--------+------------+-------------+--------------------+-------------------+\n",
      "|VendorID|lpep_pickup_datetime|lpep_dropoff_datetime|duration|total_amount|trip_distance|         minute_rate|      average_speed|\n",
      "+--------+--------------------+---------------------+--------+------------+-------------+--------------------+-------------------+\n",
      "|       2| 2019-01-01 00:10:16|  2019-01-01 00:16:32|     376|         7.3|          .86|  1.1648936170212767|   8.23404255319149|\n",
      "|       2| 2019-01-01 00:27:11|  2019-01-01 00:31:38|     267|         5.8|          .66|   1.303370786516854|  8.898876404494382|\n",
      "|       2| 2019-01-01 00:46:20|  2019-01-01 01:04:54|    1114|       19.71|         2.68|  1.0615798922800719|  8.660682226211849|\n",
      "|       2| 2019-01-01 00:19:06|  2019-01-01 00:39:43|    1237|        19.3|         4.53|  0.9361358124494745| 13.183508488278092|\n",
      "|       2| 2019-01-01 00:12:35|  2019-01-01 00:19:09|     394|         7.8|         1.05|  1.1878172588832485|  9.593908629441625|\n",
      "|       2| 2019-01-01 00:47:55|  2019-01-01 01:00:01|     726|        14.8|         3.77|  1.2231404958677687| 18.694214876033058|\n",
      "|       1| 2019-01-01 00:12:47|  2019-01-01 00:30:50|    1083|        17.3|         4.10|  0.9584487534626038| 13.628808864265928|\n",
      "|       2| 2019-01-01 00:16:23|  2019-01-01 00:39:46|    1403|        26.8|         7.75|  1.1461154668567355| 19.885958660014257|\n",
      "|       2| 2019-01-01 00:58:02|  2019-01-01 01:19:02|    1260|        16.8|         3.68|                 0.8| 10.514285714285714|\n",
      "|       2| 2019-01-01 00:37:00|  2019-01-01 00:56:42|    1182|       37.06|         6.84|  1.8812182741116754|  20.83248730964467|\n",
      "|       2| 2019-01-01 00:13:48|  2019-01-01 00:21:00|     432|        9.36|         1.15|  1.2999999999999998|  9.583333333333332|\n",
      "|       2| 2019-01-01 00:19:59|  2019-01-01 00:45:50|    1551|        16.8|          .49|  0.6499032882011606|  1.137330754352031|\n",
      "|       2| 2019-01-01 00:57:57|  2019-01-01 01:20:10|    1333|        18.3|         3.61|  0.8237059264816204|  9.749437359339835|\n",
      "|       1| 2019-01-01 00:09:02|  2019-01-01 00:17:50|     528|          14|         1.20|   1.590909090909091|  8.181818181818182|\n",
      "|       1| 2019-01-01 00:22:12|  2019-01-01 00:25:29|     197|         5.3|          .50|   1.614213197969543|  9.137055837563452|\n",
      "|       1| 2019-01-01 00:31:55|  2019-01-01 00:52:59|    1264|       24.95|         5.50|  1.1843354430379747| 15.664556962025317|\n",
      "|       2| 2019-01-01 00:30:20|  2019-01-01 00:54:19|    1439|       25.56|         5.01|  1.0657400972897844| 12.533703961084086|\n",
      "|       2| 2019-01-01 00:40:17|  2019-01-01 00:50:23|     606|       14.16|         2.72|   1.401980198019802|  16.15841584158416|\n",
      "|       2| 2019-01-01 00:51:16|  2019-01-01 01:04:10|     774|       14.75|         2.15|  1.1434108527131783|               10.0|\n",
      "|       2| 2019-01-01 00:30:28|  2019-01-01 00:41:18|     650|       14.04|         2.18|  1.2959999999999998| 12.073846153846157|\n",
      "|       2| 2019-01-01 00:26:59|  2019-01-01 01:01:33|    2074|        26.8|         6.66|  0.7753134040501447| 11.560270009643201|\n",
      "|       2| 2019-01-01 00:39:09|  2019-01-01 00:42:32|     203|         5.8|          .57|  1.7142857142857142| 10.108374384236452|\n",
      "|       2| 2019-01-01 00:35:13|  2019-01-01 01:18:53|    2620|        45.3|        13.90|  1.0374045801526717| 19.099236641221374|\n",
      "|       1| 2019-01-01 00:35:52|  2019-01-01 00:40:00|     248|         6.3|          .80|  1.5241935483870968| 11.612903225806452|\n",
      "|       1| 2019-01-01 00:48:27|  2019-01-01 00:55:49|     442|         8.3|         1.40|  1.1266968325791857| 11.402714932126695|\n",
      "|       2| 2019-01-01 00:01:21|  2019-01-01 00:08:45|     444|         8.3|         1.21|  1.1216216216216217|   9.81081081081081|\n",
      "|       2| 2019-01-01 00:24:39|  2019-01-01 00:43:17|    1118|        17.3|         2.74|  0.9284436493738819|  8.822898032200358|\n",
      "|       2| 2019-01-01 00:44:25|  2019-01-01 00:53:03|     518|       12.25|         1.99|   1.418918918918919| 13.830115830115831|\n",
      "|       2| 2019-01-01 00:44:58|  2019-01-01 01:13:46|    1728|        33.3|         9.97|  1.1562499999999998| 20.770833333333336|\n",
      "|       2| 2019-01-01 00:08:07|  2019-01-01 00:18:36|     629|         9.8|         1.36|  0.9348171701112878|  7.783783783783784|\n",
      "|       2| 2019-01-01 00:40:23|  2019-01-01 00:56:12|     949|        14.3|         2.80|   0.904109589041096| 10.621707060063223|\n",
      "|       2| 2019-01-01 00:29:46|  2019-01-01 00:41:09|     683|       12.36|         1.52|  1.0857979502196191|  8.011713030746707|\n",
      "|       2| 2019-01-01 00:01:17|  2019-01-01 00:21:33|    1216|        16.3|         3.08|  0.8042763157894737|  9.118421052631579|\n",
      "|       2| 2019-01-01 00:59:07|  2019-01-01 01:30:20|    1873|        36.8|        12.03|   1.178857447944474| 23.122263747997863|\n",
      "|       2| 2019-01-01 00:44:31|  2019-01-01 01:25:32|    2461|        43.8|        13.54|   1.067858594067452| 19.806582689963427|\n",
      "|       2| 2019-01-01 00:23:15|  2019-01-01 01:10:07|    2812|        41.3|        10.76|  0.8812233285917496|  13.77524893314367|\n",
      "|       2| 2019-01-01 00:33:41|  2019-01-01 00:54:35|    1254|       21.36|         4.58|  1.0220095693779905| 13.148325358851677|\n",
      "|       2| 2019-01-01 00:33:40|  2019-01-01 00:59:29|    1549|        22.8|         5.52|   0.883150419625565| 12.828921885087153|\n",
      "|       2| 2019-01-01 00:31:28|  2019-01-01 00:57:27|    1559|          36|        10.98|  1.3855035279025016| 25.354714560615783|\n",
      "|       1| 2019-01-01 00:19:22|  2019-01-01 00:31:33|     731|        10.8|         1.40|  0.8864569083447333|  6.894664842681259|\n",
      "|       1| 2019-01-01 00:45:24|  2019-01-01 01:09:30|    1446|        19.3|         4.10|  0.8008298755186722|  10.20746887966805|\n",
      "|       2| 2019-01-01 01:00:28|  2019-01-01 01:16:36|     968|        12.8|         2.26|  0.7933884297520662|   8.40495867768595|\n",
      "|       2| 2019-01-01 00:41:11|  2019-01-01 00:48:14|     423|       10.79|         1.43|   1.530496453900709| 12.170212765957446|\n",
      "|       2| 2019-01-01 00:55:18|  2019-01-01 00:58:08|     170|        7.25|          .76|   2.558823529411765| 16.094117647058823|\n",
      "|       1| 2019-01-01 00:18:13|  2019-01-01 00:32:22|     849|        12.3|         2.50|  0.8692579505300354|  10.60070671378092|\n",
      "|       1| 2019-01-01 00:59:59|  2019-01-01 01:12:23|     744|       15.95|         2.80|  1.2862903225806452| 13.548387096774194|\n",
      "|       2| 2019-01-01 00:51:48|  2019-01-01 00:51:58|      10|          60|          .00|               360.0|                0.0|\n",
      "|       2| 2019-01-01 00:39:04|  2019-01-01 00:52:06|     782|        14.3|         3.14|  1.0971867007672635|  14.45524296675192|\n",
      "|       2| 2019-01-01 00:47:37|  2019-01-01 00:50:08|     151|         5.8|          .74|  2.3046357615894038| 17.642384105960264|\n",
      "|       1| 2019-01-01 00:50:16|  2019-01-01 01:25:21|    2105|        32.3|         8.90|  0.9206650831353919| 15.220902612826603|\n",
      "|       2| 2019-01-01 00:23:10|  2019-01-01 00:39:03|     953|       20.76|         4.65|  1.3070304302203568|  17.56558237145855|\n",
      "|       2| 2019-01-01 00:55:59|  2019-01-01 01:48:31|    3152|        42.3|         9.98|  0.8052030456852791| 11.398477157360407|\n",
      "|       1| 2019-01-01 00:39:43|  2019-01-01 00:54:21|     878|        12.8|         2.20|  0.8747152619589977|  9.020501138952163|\n",
      "|       2| 2019-01-01 00:10:35|  2019-01-01 00:32:51|    1336|        23.3|         6.44|  1.0464071856287425| 17.353293413173656|\n",
      "|       2| 2019-01-01 00:12:41|  2019-01-01 00:29:20|     999|        12.8|         2.22|  0.7687687687687688|                8.0|\n",
      "|       2| 2019-01-01 00:44:36|  2019-01-01 00:58:31|     835|        12.3|         2.27|  0.8838323353293414|   9.78682634730539|\n",
      "|       2| 2019-01-01 01:02:35|  2019-01-01 01:07:00|     265|         6.3|          .88|  1.4264150943396225| 11.954716981132076|\n",
      "|       2| 2019-01-01 00:08:09|  2019-01-01 00:35:40|    1651|        29.8|         8.73|  1.0829800121138704|  19.03573591762568|\n",
      "|       2| 2019-01-01 00:46:27|  2019-01-01 01:22:54|    2187|       38.16|         7.00|  1.0469135802469134| 11.522633744855966|\n",
      "|       1| 2019-01-01 00:00:51|  2019-01-01 00:03:15|     144|         4.8|          .40|                 2.0| 10.000000000000002|\n",
      "|       1| 2019-01-01 00:52:26|  2019-01-01 00:59:56|     450|         8.3|         1.40|   1.106666666666667|               11.2|\n",
      "|       2| 2019-01-01 00:10:43|  2019-01-01 00:15:37|     294|        8.16|          .89|  1.6653061224489796|  10.89795918367347|\n",
      "|       2| 2019-01-01 00:18:39|  2019-01-01 00:21:48|     189|         5.3|          .29|  1.6825396825396826|  5.523809523809524|\n",
      "|       2| 2019-01-01 00:12:45|  2019-01-01 00:25:00|     735|        11.8|         2.11|   0.963265306122449|  10.33469387755102|\n",
      "|       2| 2019-01-01 00:43:00|  2019-01-01 00:53:38|     638|        13.3|         3.40|  1.2507836990595613| 19.184952978056423|\n",
      "|       2| 2019-01-01 00:39:51|  2019-01-01 00:43:05|     194|         5.8|          .75|  1.7938144329896906| 13.917525773195877|\n",
      "|       1| 2019-01-01 00:52:32|  2019-01-01 00:55:48|     196|         5.8|          .70|  1.7755102040816324| 12.857142857142856|\n",
      "|       1| 2019-01-01 00:26:04|  2019-01-01 00:42:16|     972|       27.35|         7.10|  1.6882716049382718| 26.296296296296294|\n",
      "|       1| 2019-01-01 00:36:35|  2019-01-01 00:54:14|    1059|        21.8|         6.30|  1.2351274787535411|  21.41643059490085|\n",
      "|       2| 2019-01-01 00:05:29|  2019-01-01 00:07:24|     115|         5.3|          .76|   2.765217391304348| 23.791304347826088|\n",
      "|       2| 2019-01-01 00:24:54|  2019-01-01 00:27:14|     140|         4.8|          .24|  2.0571428571428574|  6.171428571428572|\n",
      "|       2| 2019-01-01 00:28:31|  2019-01-01 00:30:24|     113|         4.8|          .39|  2.5486725663716814| 12.424778761061948|\n",
      "|       2| 2019-01-01 00:31:27|  2019-01-01 00:56:07|    1480|       41.76|        11.73|  1.6929729729729728|  28.53243243243243|\n",
      "|       2| 2019-01-01 00:38:22|  2019-01-01 00:50:32|     730|       20.16|         4.66|   1.656986301369863|  22.98082191780822|\n",
      "|       2| 2019-01-01 00:51:47|  2019-01-01 01:04:03|     736|       12.96|         1.58|  1.0565217391304347|  7.728260869565218|\n",
      "|       2| 2019-01-01 00:06:52|  2019-01-01 00:23:35|    1003|        14.3|         2.58|  0.8554336989032902|  9.260219341974079|\n",
      "|       2| 2019-01-01 00:29:33|  2019-01-01 00:37:17|     464|         9.3|         1.67|  1.2025862068965518| 12.956896551724139|\n",
      "|       2| 2019-01-01 00:51:56|  2019-01-02 00:37:17|   85521|        10.7|         1.61|0.007506928122917...|0.06777282772652331|\n",
      "|       2| 2019-01-01 01:01:56|  2019-01-01 01:21:03|    1147|        20.8|         5.75|   1.088055797733217| 18.047079337401918|\n",
      "|       2| 2019-01-01 00:00:15|  2019-01-01 00:11:28|     673|        17.3|         5.17|  1.5423476968796435| 27.655274888558694|\n",
      "|       2| 2019-01-01 00:40:18|  2019-01-01 01:03:58|    1420|        19.3|         4.36|  0.8154929577464789| 11.053521126760565|\n",
      "|       2| 2019-01-01 00:15:19|  2019-01-01 00:42:21|    1622|        26.3|         7.57|  0.9728729963008632| 16.801479654747226|\n",
      "|       2| 2019-01-01 00:56:06|  2019-01-01 01:29:56|    2030|       35.68|         9.17|  1.0545812807881774|  16.26206896551724|\n",
      "|       1| 2019-01-01 00:40:34|  2019-01-01 00:51:16|     642|       15.35|         3.40|  1.4345794392523366|  19.06542056074766|\n",
      "|       2| 2019-01-01 00:18:45|  2019-01-01 00:29:12|     627|        11.8|         2.74|  1.1291866028708135| 15.732057416267942|\n",
      "|       2| 2019-01-01 00:07:33|  2019-01-01 00:22:44|     911|        16.8|         4.49|  1.1064763995609221| 17.743139407244787|\n",
      "|       2| 2019-01-01 00:40:36|  2019-01-01 01:07:56|    1640|        32.8|        10.38|  1.1999999999999997| 22.785365853658536|\n",
      "|       2| 2019-01-01 00:06:31|  2019-01-01 00:14:44|     493|        10.3|         2.31|  1.2535496957403651|  16.86815415821501|\n",
      "|       2| 2019-01-01 00:10:50|  2019-01-01 00:11:23|      33|        5.16|          .23|   9.381818181818183| 25.090909090909093|\n",
      "|       2| 2019-01-01 00:39:52|  2019-01-01 01:11:33|    1901|        36.3|        11.25|   1.145712782745923| 21.304576538663863|\n",
      "|       2| 2019-01-01 00:21:11|  2019-01-01 00:26:29|     318|         6.8|          .81|  1.2830188679245282|  9.169811320754716|\n",
      "|       2| 2019-01-01 00:45:11|  2019-01-01 00:55:26|     615|         9.8|         1.83|  0.9560975609756099| 10.712195121951218|\n",
      "|       2| 2019-01-01 00:38:41|  2019-01-02 00:00:00|   84079|        27.8|         8.40| 0.01983848523412505| 0.3596617466906124|\n",
      "|       2| 2019-01-01 00:15:42|  2019-01-01 00:41:04|    1522|       32.76|         7.83|  1.2914586070959264| 18.520367936925098|\n",
      "|       2| 2019-01-01 00:21:28|  2019-01-01 00:48:15|    1607|        25.3|         4.49|  0.9446172993154948| 10.058494088363409|\n",
      "|       2| 2019-01-01 00:17:10|  2019-01-01 00:23:34|     384|         7.8|         1.08|             1.21875|             10.125|\n",
      "|       2| 2019-01-01 00:25:01|  2019-01-01 00:43:55|    1134|        26.3|         8.74|  1.3915343915343916| 27.746031746031747|\n",
      "|       2| 2019-01-01 00:36:56|  2019-01-01 00:43:50|     414|        10.3|         1.32|  1.4927536231884058| 11.478260869565219|\n",
      "|       2| 2019-01-01 00:53:11|  2019-01-01 00:57:04|     233|        8.91|          .68|   2.294420600858369| 10.506437768240344|\n",
      "|       2| 2019-01-01 00:01:00|  2019-01-01 00:06:27|     327|       10.38|         1.43|  1.9045871559633027| 15.743119266055043|\n",
      "+--------+--------------------+---------------------+--------+------------+-------------+--------------------+-------------------+\n",
      "only showing top 100 rows"
     ]
    }
   ],
   "source": [
    "# do some test\n",
    "tmp = df.select(['VendorID', 'lpep_pickup_datetime', 'lpep_dropoff_datetime', 'duration','total_amount','trip_distance','minute_rate','average_speed'])\n",
    "tmp.show(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "61bc3e889e8148e9802670359099e2ad",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5929810, 5779303)"
     ]
    }
   ],
   "source": [
    "dfcache.count(),df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "95ce7c361bc644f9b6fc5ebdf450ca6d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(['VendorID', 'lpep_pickup_datetime', 'lpep_dropoff_datetime', 'RatecodeID', 'PULocationID', 'DOLocationID', 'passenger_count', 'trip_distance', 'fare_amount', 'extra', 'mta_tax', 'tip_amount', 'tolls_amount', 'improvement_surcharge', 'total_amount', 'payment_type', 'trip_type', 'duration', 'minute_rate', 'average_speed'], ['VendorID', 'lpep_pickup_datetime', 'lpep_dropoff_datetime', 'RatecodeID', 'PULocationID', 'DOLocationID', 'passenger_count', 'trip_distance', 'fare_amount', 'extra', 'mta_tax', 'tip_amount', 'tolls_amount', 'improvement_surcharge', 'total_amount', 'payment_type', 'trip_type', 'duration', 'minute_rate', 'average_speed'])"
     ]
    }
   ],
   "source": [
    "df.columns,dfcache.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e24c91d88e1a44939339ad69ddab56a5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## fix colums with regular names\n",
    "for c in df.columns:\n",
    "    df = df.withColumnRenamed(c, c.replace(' ', '_'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e9535393c7e44df2bbbe58773096bb7a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------+--------------------+---------------------+----------+------------+------------+---------------+-------------+-----------+-----+-------+----------+------------+---------------------+------------+------------+---------+--------+-----------+-------------+\n",
      "|VendorID|lpep_pickup_datetime|lpep_dropoff_datetime|RatecodeID|PULocationID|DOLocationID|passenger_count|trip_distance|fare_amount|extra|mta_tax|tip_amount|tolls_amount|improvement_surcharge|total_amount|payment_type|trip_type|duration|minute_rate|average_speed|\n",
      "+--------+--------------------+---------------------+----------+------------+------------+---------------+-------------+-----------+-----+-------+----------+------------+---------------------+------------+------------+---------+--------+-----------+-------------+\n",
      "|       2| 2019-01-01 00:10:16|  2019-01-01 00:16:32|         1|          97|          49|              2|          .86|          6|  0.5|    0.5|         0|           0|                  0.3|         7.3|           2|        1|     376|     1.1649|        8.234|\n",
      "|       2| 2019-01-01 00:27:11|  2019-01-01 00:31:38|         1|          49|         189|              2|          .66|        4.5|  0.5|    0.5|         0|           0|                  0.3|         5.8|           1|        1|     267|     1.3034|       8.8989|\n",
      "|       2| 2019-01-01 00:46:20|  2019-01-01 01:04:54|         1|         189|          17|              2|         2.68|       13.5|  0.5|    0.5|      2.96|           0|                  0.3|       19.71|           1|        1|    1114|     1.0616|       8.6607|\n",
      "|       2| 2019-01-01 00:19:06|  2019-01-01 00:39:43|         1|          82|         258|              1|         4.53|         18|  0.5|    0.5|         0|           0|                  0.3|        19.3|           2|        1|    1237|     0.9361|      13.1835|\n",
      "|       2| 2019-01-01 00:12:35|  2019-01-01 00:19:09|         1|          49|          17|              1|         1.05|        6.5|  0.5|    0.5|         0|           0|                  0.3|         7.8|           1|        1|     394|     1.1878|       9.5939|\n",
      "|       2| 2019-01-01 00:47:55|  2019-01-01 01:00:01|         1|         255|          33|              1|         3.77|       13.5|  0.5|    0.5|         0|           0|                  0.3|        14.8|           1|        1|     726|     1.2231|      18.6942|\n",
      "|       1| 2019-01-01 00:12:47|  2019-01-01 00:30:50|         1|          76|         225|              1|         4.10|         16|  0.5|    0.5|         0|           0|                  0.3|        17.3|           1|        1|    1083|     0.9584|      13.6288|\n",
      "|       2| 2019-01-01 00:16:23|  2019-01-01 00:39:46|         1|          25|          89|              1|         7.75|       25.5|  0.5|    0.5|         0|           0|                  0.3|        26.8|           1|        1|    1403|     1.1461|       19.886|\n",
      "|       2| 2019-01-01 00:58:02|  2019-01-01 01:19:02|         1|          85|          39|              1|         3.68|       15.5|  0.5|    0.5|         0|           0|                  0.3|        16.8|           1|        1|    1260|        0.8|      10.5143|\n",
      "|       2| 2019-01-01 00:37:00|  2019-01-01 00:56:42|         1|         223|         238|              1|         6.84|         22|  0.5|    0.5|         8|        5.76|                  0.3|       37.06|           1|        1|    1182|     1.8812|      20.8325|\n",
      "+--------+--------------------+---------------------+----------+------------+------------+---------------+-------------+-----------+-----+-------+----------+------------+---------------------+------------+------------+---------+--------+-----------+-------------+\n",
      "only showing top 10 rows"
     ]
    }
   ],
   "source": [
    "## polish data\n",
    "\n",
    "## remove data which is tooooo small and ilegal value\n",
    "df = df.filter(df['trip_distance'] >= 0.001)\n",
    "\n",
    "## round minute_rate and average_speed to scale 3 \n",
    "\n",
    "df = df.withColumn('minute_rate', f.round(df['minute_rate'],4))\n",
    "\n",
    "df = df.withColumn('average_speed', f.round(df['average_speed'],4))\n",
    "\n",
    "# rename 'VendorID'\n",
    "# df = df.withColumnRenamed('VendorID', 'vendor_id')\n",
    "\n",
    "# rename passenger_count to pcount shortly\n",
    "df = df.withColumnRenamed('passenger_count', 'p_count')\n",
    "\n",
    "\n",
    "\n",
    "df.show(10)\n",
    "\n",
    "# tmp = df.select(['VendorID', 'lpep_pickup_datetime', 'lpep_dropoff_datetime', 'duration','total_amount','trip_distance','minute_rate','average_speed'])\n",
    "\n",
    "# tmp.show(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
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   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4ac90e9d2eef430590aacf97629ad046",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------+--------------------+---------------------+--------+------------+-------------+-----------+-------------+\n",
      "|VendorID|lpep_pickup_datetime|lpep_dropoff_datetime|duration|total_amount|trip_distance|minute_rate|average_speed|\n",
      "+--------+--------------------+---------------------+--------+------------+-------------+-----------+-------------+\n",
      "|       2| 2019-01-01 00:10:16|  2019-01-01 00:16:32|     376|         7.3|          .86|     1.1649|        8.234|\n",
      "|       2| 2019-01-01 00:27:11|  2019-01-01 00:31:38|     267|         5.8|          .66|     1.3034|       8.8989|\n",
      "|       2| 2019-01-01 00:46:20|  2019-01-01 01:04:54|    1114|       19.71|         2.68|     1.0616|       8.6607|\n",
      "|       2| 2019-01-01 00:19:06|  2019-01-01 00:39:43|    1237|        19.3|         4.53|     0.9361|      13.1835|\n",
      "|       2| 2019-01-01 00:12:35|  2019-01-01 00:19:09|     394|         7.8|         1.05|     1.1878|       9.5939|\n",
      "|       2| 2019-01-01 00:47:55|  2019-01-01 01:00:01|     726|        14.8|         3.77|     1.2231|      18.6942|\n",
      "|       1| 2019-01-01 00:12:47|  2019-01-01 00:30:50|    1083|        17.3|         4.10|     0.9584|      13.6288|\n",
      "|       2| 2019-01-01 00:16:23|  2019-01-01 00:39:46|    1403|        26.8|         7.75|     1.1461|       19.886|\n",
      "|       2| 2019-01-01 00:58:02|  2019-01-01 01:19:02|    1260|        16.8|         3.68|        0.8|      10.5143|\n",
      "|       2| 2019-01-01 00:37:00|  2019-01-01 00:56:42|    1182|       37.06|         6.84|     1.8812|      20.8325|\n",
      "|       2| 2019-01-01 00:13:48|  2019-01-01 00:21:00|     432|        9.36|         1.15|        1.3|       9.5833|\n",
      "|       2| 2019-01-01 00:19:59|  2019-01-01 00:45:50|    1551|        16.8|          .49|     0.6499|       1.1373|\n",
      "|       2| 2019-01-01 00:57:57|  2019-01-01 01:20:10|    1333|        18.3|         3.61|     0.8237|       9.7494|\n",
      "|       1| 2019-01-01 00:09:02|  2019-01-01 00:17:50|     528|          14|         1.20|     1.5909|       8.1818|\n",
      "|       1| 2019-01-01 00:22:12|  2019-01-01 00:25:29|     197|         5.3|          .50|     1.6142|       9.1371|\n",
      "|       1| 2019-01-01 00:31:55|  2019-01-01 00:52:59|    1264|       24.95|         5.50|     1.1843|      15.6646|\n",
      "|       2| 2019-01-01 00:30:20|  2019-01-01 00:54:19|    1439|       25.56|         5.01|     1.0657|      12.5337|\n",
      "|       2| 2019-01-01 00:40:17|  2019-01-01 00:50:23|     606|       14.16|         2.72|      1.402|      16.1584|\n",
      "|       2| 2019-01-01 00:51:16|  2019-01-01 01:04:10|     774|       14.75|         2.15|     1.1434|         10.0|\n",
      "|       2| 2019-01-01 00:30:28|  2019-01-01 00:41:18|     650|       14.04|         2.18|      1.296|      12.0738|\n",
      "+--------+--------------------+---------------------+--------+------------+-------------+-----------+-------------+\n",
      "only showing top 20 rows"
     ]
    }
   ],
   "source": [
    "tmp = df.select(['VendorID', 'lpep_pickup_datetime', 'lpep_dropoff_datetime', 'duration','total_amount','trip_distance','minute_rate','average_speed'])\n",
    "tmp.show(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7cfb59ad8b4d46e6a940526f053e2a1f",
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       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Write Data Back to S3 using AWS API\n",
    "currenttime = datetime.datetime.now()\n",
    "\n",
    "df.write.option('compression', 'snappy') \\\n",
    "    .save( \"s3://data-etl-o-target-0/result/green/green_car_data_processed_{date}/\".format(date=currenttime.strftime('%Y%m%d%H%M%s')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e980c3c4cb4942cc80f17b7543366e05",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
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       "version_minor": 0
      },
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#read in our raw dataset\n",
    "dfz = spark.read.option('header', 'true') \\\n",
    "    .option('mode', 'FAILFAST') \\\n",
    "    .option('columnNameOfCorruptRecord', 'error') \\\n",
    "    .csv('s3://data-etl-o-original-raw/zone/nyc.tlc.taxi_zone_lookup.csv') \\"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4f3194fc8cbd497685b1ea241be6145a",
       "version_major": 2,
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      },
      "text/plain": [
       "VBox()"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_minor": 0
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(265, ['LocationID', 'Borough', 'Zone', 'service_zone'])"
     ]
    }
   ],
   "source": [
    "dfzcache = dfz.cache()\n",
    "\n",
    "dfz.count(),dfz.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "06db0e1183bc43d8b4af2da12e93e851",
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       "version_minor": 0
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       "VBox()"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+-------------+--------------------+------------+\n",
      "|LocationID|      Borough|                Zone|service_zone|\n",
      "+----------+-------------+--------------------+------------+\n",
      "|         1|          EWR|      Newark Airport|         EWR|\n",
      "|         2|       Queens|         Jamaica Bay|   Boro Zone|\n",
      "|         3|        Bronx|Allerton/Pelham G...|   Boro Zone|\n",
      "|         4|    Manhattan|       Alphabet City| Yellow Zone|\n",
      "|         5|Staten Island|       Arden Heights|   Boro Zone|\n",
      "|         6|Staten Island|Arrochar/Fort Wad...|   Boro Zone|\n",
      "|         7|       Queens|             Astoria|   Boro Zone|\n",
      "|         8|       Queens|        Astoria Park|   Boro Zone|\n",
      "|         9|       Queens|          Auburndale|   Boro Zone|\n",
      "|        10|       Queens|        Baisley Park|   Boro Zone|\n",
      "|        11|     Brooklyn|          Bath Beach|   Boro Zone|\n",
      "|        12|    Manhattan|        Battery Park| Yellow Zone|\n",
      "|        13|    Manhattan|   Battery Park City| Yellow Zone|\n",
      "|        14|     Brooklyn|           Bay Ridge|   Boro Zone|\n",
      "|        15|       Queens|Bay Terrace/Fort ...|   Boro Zone|\n",
      "|        16|       Queens|             Bayside|   Boro Zone|\n",
      "|        17|     Brooklyn|             Bedford|   Boro Zone|\n",
      "|        18|        Bronx|        Bedford Park|   Boro Zone|\n",
      "|        19|       Queens|           Bellerose|   Boro Zone|\n",
      "|        20|        Bronx|             Belmont|   Boro Zone|\n",
      "+----------+-------------+--------------------+------------+\n",
      "only showing top 20 rows"
     ]
    }
   ],
   "source": [
    "dfz.show(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "447bb51c711c4edbb95a6942c17d08f6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+\n",
      "|      Borough|\n",
      "+-------------+\n",
      "|       Queens|\n",
      "|          EWR|\n",
      "|      Unknown|\n",
      "|     Brooklyn|\n",
      "|Staten Island|\n",
      "|    Manhattan|\n",
      "|        Bronx|\n",
      "+-------------+"
     ]
    }
   ],
   "source": [
    "dfz.select('Borough').distinct().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "222f6e0b800b40b8acdf4bef9da2a3cd",
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      "text/plain": [
       "VBox()"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+-------------+--------------------+------------+\n",
      "|LocationID|      Borough|                Zone|service_zone|\n",
      "+----------+-------------+--------------------+------------+\n",
      "|         1|          EWR|      Newark Airport|         EWR|\n",
      "|         2|       Queens|         Jamaica Bay|   Boro Zone|\n",
      "|         3|        Bronx|Allerton/Pelham G...|   Boro Zone|\n",
      "|         4|    Manhattan|       Alphabet City| Yellow Zone|\n",
      "|         5|Staten Island|       Arden Heights|   Boro Zone|\n",
      "|         6|Staten Island|Arrochar/Fort Wad...|   Boro Zone|\n",
      "|         7|       Queens|             Astoria|   Boro Zone|\n",
      "|         8|       Queens|        Astoria Park|   Boro Zone|\n",
      "|         9|       Queens|          Auburndale|   Boro Zone|\n",
      "|        10|       Queens|        Baisley Park|   Boro Zone|\n",
      "|        11|     Brooklyn|          Bath Beach|   Boro Zone|\n",
      "|        12|    Manhattan|        Battery Park| Yellow Zone|\n",
      "|        13|    Manhattan|   Battery Park City| Yellow Zone|\n",
      "|        14|     Brooklyn|           Bay Ridge|   Boro Zone|\n",
      "|        15|       Queens|Bay Terrace/Fort ...|   Boro Zone|\n",
      "|        16|       Queens|             Bayside|   Boro Zone|\n",
      "|        17|     Brooklyn|             Bedford|   Boro Zone|\n",
      "|        18|        Bronx|        Bedford Park|   Boro Zone|\n",
      "|        19|       Queens|           Bellerose|   Boro Zone|\n",
      "|        20|        Bronx|             Belmont|   Boro Zone|\n",
      "+----------+-------------+--------------------+------------+\n",
      "only showing top 20 rows"
     ]
    }
   ],
   "source": [
    "dfz.show(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c00e64f89c6847f1a82359846e28b843",
       "version_major": 2,
       "version_minor": 0
      },
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       "VBox()"
      ]
     },
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    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+--------------+\n",
      "|      Borough|count(Borough)|\n",
      "+-------------+--------------+\n",
      "|       Queens|            69|\n",
      "|          EWR|             1|\n",
      "|      Unknown|             2|\n",
      "|     Brooklyn|            61|\n",
      "|Staten Island|            20|\n",
      "|    Manhattan|            69|\n",
      "|        Bronx|            43|\n",
      "+-------------+--------------+"
     ]
    }
   ],
   "source": [
    "dfz.select('Borough').groupBy('Borough').agg({'Borough': 'count'}).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
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    "jupyter": {
     "outputs_hidden": true
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# packages in environment at /opt/conda:\n",
      "#\n",
      "# Name                    Version                   Build  Channel\n",
      "_libgcc_mutex             0.1                 conda_forge    conda-forge\n",
      "_openmp_mutex             4.5                      1_llvm    conda-forge\n",
      "alabaster                 0.7.12                     py_0    defaults\n",
      "anaconda-client           1.7.2                    py37_0    defaults\n",
      "anaconda-navigator        1.9.7                    py37_0    defaults\n",
      "anaconda-project          0.8.4                      py_0    defaults\n",
      "asn1crypto                1.3.0                    py37_0    defaults\n",
      "astroid                   2.3.3                    py37_0    defaults\n",
      "astropy                   4.0              py37h7b6447c_0    defaults\n",
      "attrs                     19.3.0                     py_0    defaults\n",
      "autovizwidget             0.13.1                     py_0    defaults\n",
      "awscli                    1.18.3                   py37_0    conda-forge\n",
      "awseditorssparkmonitoringwidget 1.0                      py37_0    file:///tmp/conda-repo\n",
      "awseditorsstorage         0.0.1                    py37_0    file:///tmp/conda-repo\n",
      "babel                     2.8.0                      py_0    defaults\n",
      "backcall                  0.1.0                    py37_0    defaults\n",
      "backports                 1.0                        py_2    defaults\n",
      "backports.functools_lru_cache 1.6.1                      py_0    defaults\n",
      "backports.shutil_get_terminal_size 1.0.0                    py37_2    defaults\n",
      "backports.tempfile        1.0                        py_1    defaults\n",
      "backports.weakref         1.0.post1                  py_1    defaults\n",
      "beautifulsoup4            4.8.2                    py37_0    defaults\n",
      "bitarray                  1.2.1            py37h7b6447c_0    defaults\n",
      "bkcharts                  0.2                      py37_0    defaults\n",
      "blas                      1.0                         mkl    defaults\n",
      "blaze                     0.11.3                   py37_0    defaults\n",
      "bleach                    3.1.0                      py_0    defaults\n",
      "blosc                     1.14.3               hdbcaa40_0    defaults\n",
      "bokeh                     1.4.0                    py37_0    defaults\n",
      "boto                      2.49.0                   py37_0    defaults\n",
      "botocore                  1.15.3                     py_0    conda-forge\n",
      "bottleneck                1.3.1            py37hdd07704_0    defaults\n",
      "bzip2                     1.0.6                h14c3975_5    defaults\n",
      "ca-certificates           2020.1.1                      0    defaults\n",
      "cairo                     1.14.12              h8948797_3    defaults\n",
      "certifi                   2019.11.28               py37_0    defaults\n",
      "cffi                      1.14.0           py37h2e261b9_0    defaults\n",
      "chardet                   3.0.4                 py37_1003    defaults\n",
      "click                     7.0                        py_0    defaults\n",
      "cloudpickle               1.3.0                      py_0    defaults\n",
      "clyent                    1.2.2                    py37_1    defaults\n",
      "colorama                  0.4.3                      py_0    defaults\n",
      "conda                     4.8.2                    py37_0    defaults\n",
      "conda-build               3.18.11                  py37_0    defaults\n",
      "conda-env                 2.6.0                         1    conda-forge\n",
      "conda-package-handling    1.6.0            py37h7b6447c_0    defaults\n",
      "conda-verify              3.4.2                      py_1    defaults\n",
      "contextlib2               0.6.0.post1                py_0    defaults\n",
      "cryptography              2.8              py37h1ba5d50_0    defaults\n",
      "curl                      7.68.0               hf8cf82a_0    conda-forge\n",
      "cycler                    0.10.0                   py37_0    defaults\n",
      "cython                    0.29.15          py37he6710b0_0    defaults\n",
      "cytoolz                   0.10.1           py37h7b6447c_0    defaults\n",
      "dask                      2.10.1                     py_0    defaults\n",
      "dask-core                 2.10.1                     py_0    defaults\n",
      "datashape                 0.5.4                    py37_1    defaults\n",
      "dbus                      1.13.2               h714fa37_1    defaults\n",
      "decorator                 4.4.1                      py_0    defaults\n",
      "defusedxml                0.6.0                      py_0    defaults\n",
      "distributed               2.10.0                     py_0    defaults\n",
      "docutils                  0.15.2                   py37_0    defaults\n",
      "entrypoints               0.3                      py37_0    defaults\n",
      "et_xmlfile                1.0.1                    py37_0    defaults\n",
      "expat                     2.2.6                he6710b0_0    defaults\n",
      "fastcache                 1.1.0            py37h7b6447c_0    defaults\n",
      "filelock                  3.0.12                     py_0    defaults\n",
      "flask                     1.1.1                      py_0    defaults\n",
      "flask-cors                3.0.8                      py_0    defaults\n",
      "fontconfig                2.13.0               h9420a91_0    defaults\n",
      "freetype                  2.9.1                h8a8886c_1    defaults\n",
      "fribidi                   1.0.5                h7b6447c_0    defaults\n",
      "fsspec                    0.6.2                      py_0    defaults\n",
      "future                    0.18.2                   py37_0    defaults\n",
      "get_terminal_size         1.0.0                haa9412d_0    defaults\n",
      "gevent                    1.4.0            py37h7b6447c_0    defaults\n",
      "gitdb2                    2.0.6                      py_0    defaults\n",
      "gitpython                 3.0.5                      py_0    defaults\n",
      "glib                      2.56.2               hd408876_0    defaults\n",
      "glob2                     0.7                        py_0    defaults\n",
      "gmp                       6.1.2                h6c8ec71_1    defaults\n",
      "gmpy2                     2.0.8            py37h10f8cd9_2    defaults\n",
      "graphite2                 1.3.11               h16798f4_2    defaults\n",
      "greenlet                  0.4.15           py37h7b6447c_0    defaults\n",
      "gst-plugins-base          1.14.0               hbbd80ab_1    defaults\n",
      "gstreamer                 1.14.0               hb453b48_1    defaults\n",
      "h5py                      2.8.0            py37h989c5e5_3    defaults\n",
      "harfbuzz                  1.8.8                hffaf4a1_0    defaults\n",
      "hdf5                      1.10.2               hba1933b_1    defaults\n",
      "hdijupyterutils           0.12.9                     py_0    defaults\n",
      "heapdict                  1.0.1                      py_0    defaults\n",
      "html5lib                  1.0.1                      py_0    defaults\n",
      "hypothesis                5.5.4                      py_0    defaults\n",
      "icu                       58.2                 h9c2bf20_1    defaults\n",
      "idna                      2.8                      py37_0    defaults\n",
      "imageio                   2.6.1                    py37_0    defaults\n",
      "imagesize                 1.2.0                      py_0    defaults\n",
      "importlib_metadata        1.5.0                    py37_0    defaults\n",
      "intel-openmp              2020.0                      166    defaults\n",
      "ipykernel                 5.1.4            py37h39e3cac_0    defaults\n",
      "ipython                   7.12.0           py37h5ca1d4c_0    defaults\n",
      "ipython_genutils          0.2.0                    py37_0    defaults\n",
      "ipywidgets                7.5.1                      py_0    defaults\n",
      "isort                     4.3.21                   py37_0    defaults\n",
      "itsdangerous              1.1.0                      py_0    defaults\n",
      "jbig                      2.1                  hdba287a_0    defaults\n",
      "jdcal                     1.4.1                      py_0    defaults\n",
      "jedi                      0.16.0                   py37_0    defaults\n",
      "jeepney                   0.4.2                      py_0    defaults\n",
      "jinja2                    2.11.1                     py_0    defaults\n",
      "jmespath                  0.9.4                      py_0    conda-forge\n",
      "joblib                    0.14.1                     py_0    defaults\n",
      "jpeg                      9b                   h024ee3a_2    defaults\n",
      "json5                     0.9.1                      py_0    defaults\n",
      "jsonschema                3.2.0                    py37_0    defaults\n",
      "jupyter_client            5.3.4                    py37_0    defaults\n",
      "jupyter_console           6.1.0                      py_0    defaults\n",
      "jupyter_core              4.6.1                    py37_0    defaults\n",
      "jupyterlab                1.2.3              pyhf63ae98_0    defaults\n",
      "jupyterlab-git            0.9.0                      py_0    conda-forge\n",
      "jupyterlab_server         1.0.0                      py_0    defaults\n",
      "keyring                   21.1.0                   py37_0    defaults\n",
      "kiwisolver                1.1.0            py37he6710b0_0    defaults\n",
      "krb5                      1.16.4               h2fd8d38_0    conda-forge\n",
      "lazy-object-proxy         1.4.3            py37h7b6447c_0    defaults\n",
      "libarchive                3.3.3             hb44662c_1005    conda-forge\n",
      "libblas                   3.8.0                    15_mkl    conda-forge\n",
      "libcblas                  3.8.0                    15_mkl    conda-forge\n",
      "libcurl                   7.68.0               hda55be3_0    conda-forge\n",
      "libedit                   3.1.20170329         h6b74fdf_2    defaults\n",
      "libffi                    3.2.1                hd88cf55_4    defaults\n",
      "libgcc-ng                 9.2.0                h24d8f2e_2    conda-forge\n",
      "libgfortran-ng            7.3.0                hdf63c60_5    conda-forge\n",
      "libiconv                  1.15              h516909a_1005    conda-forge\n",
      "liblapack                 3.8.0                    15_mkl    conda-forge\n",
      "liblapacke                3.8.0                    15_mkl    conda-forge\n",
      "liblief                   0.9.0                hf8a498c_1    conda-forge\n",
      "libopenblas               0.3.8                h5ec1e0e_0    conda-forge\n",
      "libpng                    1.6.37               hbc83047_0    defaults\n",
      "libsodium                 1.0.16               h1bed415_0    defaults\n",
      "libssh2                   1.8.2                h22169c7_2    conda-forge\n",
      "libstdcxx-ng              9.2.0                hdf63c60_2    conda-forge\n",
      "libtiff                   4.0.9                he85c1e1_1    defaults\n",
      "libtool                   2.4.6                h544aabb_3    defaults\n",
      "libuuid                   1.0.3                h1bed415_2    defaults\n",
      "libuv                     1.34.0               h516909a_0    conda-forge\n",
      "libxcb                    1.13                 h1bed415_1    defaults\n",
      "libxml2                   2.9.9                hea5a465_1    defaults\n",
      "libxslt                   1.1.33               h7d1a2b0_0    defaults\n",
      "llvm-openmp               9.0.1                hc9558a2_2    conda-forge\n",
      "llvmlite                  0.31.0           py37hd408876_0    defaults\n",
      "locket                    0.2.0                    py37_1    defaults\n",
      "lxml                      4.5.0            py37hefd8a0e_0    defaults\n",
      "lz4-c                     1.8.3             he1b5a44_1001    conda-forge\n",
      "lzo                       2.10                 h49e0be7_2    defaults\n",
      "markupsafe                1.1.1            py37h7b6447c_0    defaults\n",
      "matplotlib                3.1.3                    py37_0    defaults\n",
      "matplotlib-base           3.1.3            py37hef1b27d_0    defaults\n",
      "mccabe                    0.6.1                    py37_1    defaults\n",
      "mistune                   0.8.4            py37h7b6447c_0    defaults\n",
      "mkl                       2020.0                      166    conda-forge\n",
      "mkl-service               2.3.0            py37he904b0f_0    defaults\n",
      "mkl_fft                   1.0.15           py37ha843d7b_0    defaults\n",
      "mkl_random                1.1.0            py37hd6b4f25_0    defaults\n",
      "more-itertools            8.2.0                      py_0    defaults\n",
      "mpc                       1.1.0                h10f8cd9_1    defaults\n",
      "mpfr                      4.0.1                hdf1c602_3    defaults\n",
      "mpmath                    1.1.0                    py37_0    defaults\n",
      "msgpack-python            0.6.1            py37hfd86e86_1    defaults\n",
      "multipledispatch          0.6.0                    py37_0    defaults\n",
      "navigator-updater         0.2.1                    py37_0    defaults\n",
      "nbconvert                 5.6.1                    py37_0    defaults\n",
      "nbdime                    1.1.0                    py37_0    conda-forge\n",
      "nbformat                  5.0.4                      py_0    defaults\n",
      "ncurses                   6.1                  hf484d3e_0    defaults\n",
      "networkx                  2.4                        py_0    defaults\n",
      "nltk                      3.4.5                    py37_0    defaults\n",
      "nodejs                    10.13.0              he6710b0_0    defaults\n",
      "nose                      1.3.7                    py37_2    defaults\n",
      "notebook                  6.0.2                    py37_0    defaults\n",
      "numba                     0.48.0           py37h0573a6f_0    defaults\n",
      "numexpr                   2.7.1            py37h423224d_0    defaults\n",
      "numpy                     1.18.1           py37h4f9e942_0    defaults\n",
      "numpy-base                1.18.1           py37hde5b4d6_1    defaults\n",
      "numpydoc                  0.9.2                      py_0    defaults\n",
      "odo                       0.5.1                    py37_0    defaults\n",
      "olefile                   0.46                       py_0    defaults\n",
      "openpyxl                  3.0.3                      py_0    defaults\n",
      "openssl                   1.1.1d               h7b6447c_4    defaults\n",
      "packaging                 20.1                       py_0    defaults\n",
      "pandas                    1.0.1            py37h0573a6f_0    defaults\n",
      "pandoc                    1.19.2.1             hea2e7c5_1    defaults\n",
      "pandocfilters             1.4.2                    py37_1    defaults\n",
      "pango                     1.42.3               h8589676_0    defaults\n",
      "parso                     0.6.1                      py_0    defaults\n",
      "partd                     1.1.0                      py_0    defaults\n",
      "patchelf                  0.9                  hf79760b_2    defaults\n",
      "path                      13.1.0                   py37_0    defaults\n",
      "path.py                   12.4.0                        0    defaults\n",
      "pathlib2                  2.3.5                    py37_0    defaults\n",
      "patsy                     0.5.1                    py37_0    defaults\n",
      "pcre                      8.42                 h439df22_0    defaults\n",
      "pep8                      1.7.1                    py37_0    defaults\n",
      "pexpect                   4.8.0                    py37_0    defaults\n",
      "pickleshare               0.7.5                    py37_0    defaults\n",
      "pillow                    5.4.1            py37h34e0f95_0    defaults\n",
      "pip                       20.0.2                   py37_1    defaults\n",
      "pixman                    0.38.0               h7b6447c_0    defaults\n",
      "pkginfo                   1.5.0.1                  py37_0    defaults\n",
      "plotly                    4.5.0                      py_0    defaults\n",
      "pluggy                    0.13.1                   py37_0    defaults\n",
      "ply                       3.11                     py37_0    defaults\n",
      "prometheus_client         0.7.1                      py_0    defaults\n",
      "prompt_toolkit            3.0.3                      py_0    defaults\n",
      "psutil                    5.6.7            py37h7b6447c_0    defaults\n",
      "ptyprocess                0.6.0                    py37_0    defaults\n",
      "py                        1.8.1                      py_0    defaults\n",
      "py-lief                   0.9.0            py37he1b5a44_1    conda-forge\n",
      "pyasn1                    0.4.8                      py_0    conda-forge\n",
      "pycodestyle               2.5.0                    py37_0    defaults\n",
      "pycosat                   0.6.3            py37h7b6447c_0    defaults\n",
      "pycparser                 2.19                       py_0    defaults\n",
      "pycrypto                  2.6.1            py37h14c3975_9    defaults\n",
      "pycurl                    7.43.0.5         py37h1ba5d50_0    defaults\n",
      "pyflakes                  2.1.1                    py37_0    defaults\n",
      "pygments                  2.5.2                      py_0    defaults\n",
      "pykerberos                1.2.1            py37h14c3975_0    defaults\n",
      "pylint                    2.4.4                    py37_0    defaults\n",
      "pyodbc                    4.0.24           py37he6710b0_0    defaults\n",
      "pyopenssl                 19.1.0                   py37_0    defaults\n",
      "pyparsing                 2.4.6                      py_0    defaults\n",
      "pyqt                      5.6.0            py37h22d08a2_6    defaults\n",
      "pyrsistent                0.15.7           py37h7b6447c_0    defaults\n",
      "pysocks                   1.7.1                    py37_0    defaults\n",
      "pytables                  3.4.4            py37ha205bf6_0    defaults\n",
      "pytest                    5.3.5                    py37_0    defaults\n",
      "pytest-arraydiff          0.3              py37h39e3cac_0    defaults\n",
      "pytest-astropy            0.8.0                      py_0    defaults\n",
      "pytest-astropy-header     0.1.2                      py_0    defaults\n",
      "pytest-doctestplus        0.5.0                      py_0    defaults\n",
      "pytest-openfiles          0.4.0                      py_0    defaults\n",
      "pytest-remotedata         0.3.2                    py37_0    defaults\n",
      "pytest-runner             5.2                        py_0    defaults\n",
      "python                    3.7.3                h0371630_0    defaults\n",
      "python-dateutil           2.8.1                      py_0    defaults\n",
      "python-libarchive-c       2.8                     py37_13    defaults\n",
      "pytz                      2019.3                     py_0    defaults\n",
      "pywavelets                1.1.1            py37h7b6447c_0    defaults\n",
      "pyyaml                    5.2              py37h7b6447c_0    defaults\n",
      "pyzmq                     18.1.1                   pypi_0    pypi\n",
      "qt                        5.6.3                h8bf5577_3    defaults\n",
      "qtawesome                 0.6.1                      py_0    defaults\n",
      "qtconsole                 4.6.0                      py_1    defaults\n",
      "qtpy                      1.9.0                      py_0    defaults\n",
      "readline                  7.0                  ha6073c6_4    defaults\n",
      "requests                  2.22.0                   py37_1    defaults\n",
      "requests-kerberos         0.12.0                   py37_0    defaults\n",
      "retrying                  1.3.3                      py_2    defaults\n",
      "ripgrep                   11.0.2               h516909a_3    conda-forge\n",
      "rope                      0.16.0                     py_0    defaults\n",
      "rsa                       3.4.2                      py_1    conda-forge\n",
      "ruamel_yaml               0.15.87          py37h7b6447c_0    defaults\n",
      "s3transfer                0.3.3                    py37_0    defaults\n",
      "scikit-image              0.16.2           py37h0573a6f_0    defaults\n",
      "scikit-learn              0.22.1           py37hd81dba3_0    defaults\n",
      "scipy                     1.4.1            py37h0b6359f_0    defaults\n",
      "seaborn                   0.10.0                     py_0    defaults\n",
      "secretstorage             3.1.2                    py37_0    defaults\n",
      "send2trash                1.5.0                    py37_0    defaults\n",
      "setuptools                45.2.0                   py37_0    defaults\n",
      "simplegeneric             0.8.1                    py37_2    defaults\n",
      "singledispatch            3.4.0.3                  py37_0    defaults\n",
      "sip                       4.18.1           py37hf484d3e_2    defaults\n",
      "six                       1.14.0                   py37_0    defaults\n",
      "smmap2                    2.0.5                      py_0    defaults\n",
      "snappy                    1.1.7                hbae5bb6_3    defaults\n",
      "snowballstemmer           2.0.0                      py_0    defaults\n",
      "sortedcollections         1.1.2                    py37_0    defaults\n",
      "sortedcontainers          2.1.0                    py37_0    defaults\n",
      "soupsieve                 1.9.5                    py37_0    defaults\n",
      "sparkmagic                0.12.9.amzn.0              py37    file:///tmp/conda-repo\n",
      "sphinx                    2.4.0                      py_0    defaults\n",
      "sphinxcontrib             1.0                      py37_1    defaults\n",
      "sphinxcontrib-applehelp   1.0.1                      py_0    defaults\n",
      "sphinxcontrib-devhelp     1.0.1                      py_0    defaults\n",
      "sphinxcontrib-htmlhelp    1.0.2                      py_0    defaults\n",
      "sphinxcontrib-jsmath      1.0.1                      py_0    defaults\n",
      "sphinxcontrib-qthelp      1.0.2                      py_0    defaults\n",
      "sphinxcontrib-serializinghtml 1.1.3                      py_0    defaults\n",
      "sphinxcontrib-websupport  1.2.0                      py_0    defaults\n",
      "spyder                    3.3.6                    py37_0    defaults\n",
      "spyder-kernels            0.5.2                    py37_0    defaults\n",
      "sqlalchemy                1.3.13           py37h7b6447c_0    defaults\n",
      "sqlite                    3.28.0               h8b20d00_0    conda-forge\n",
      "statsmodels               0.11.0           py37h7b6447c_0    defaults\n",
      "sympy                     1.5.1                    py37_0    defaults\n",
      "tbb                       2020.0               hfd86e86_0    defaults\n",
      "tblib                     1.6.0                      py_0    defaults\n",
      "terminado                 0.8.3                    py37_0    defaults\n",
      "testpath                  0.4.4                      py_0    defaults\n",
      "tk                        8.6.10               hed695b0_0    conda-forge\n",
      "toolz                     0.10.0                     py_0    defaults\n",
      "tornado                   6.0.3            py37h7b6447c_3    defaults\n",
      "tqdm                      4.43.0                     py_0    conda-forge\n",
      "traitlets                 4.3.3                    py37_0    defaults\n",
      "typing                    3.6.4                    py37_0    defaults\n",
      "unicodecsv                0.14.1                   py37_0    defaults\n",
      "unixodbc                  2.3.6                h1bed415_0    defaults\n",
      "urllib3                   1.25.8                   py37_0    defaults\n",
      "wcwidth                   0.1.8                      py_0    defaults\n",
      "webencodings              0.5.1                    py37_1    defaults\n",
      "werkzeug                  1.0.0                      py_0    defaults\n",
      "wheel                     0.34.2                   py37_0    defaults\n",
      "widgetsnbextension        3.5.1                    py37_0    defaults\n",
      "wrapt                     1.11.2           py37h7b6447c_0    defaults\n",
      "wurlitzer                 2.0.0                    py37_0    defaults\n",
      "xlrd                      1.2.0                      py_0    defaults\n",
      "xlsxwriter                1.2.7                      py_0    defaults\n",
      "xlwt                      1.3.0                    py37_0    defaults\n",
      "xz                        5.2.4                h14c3975_4    defaults\n",
      "yaml                      0.1.7                had09818_2    defaults\n",
      "zeromq                    4.2.5                h439df22_0    defaults\n",
      "zict                      1.0.0                      py_0    defaults\n",
      "zipp                      2.2.0                      py_0    defaults\n",
      "zlib                      1.2.11               ha838bed_2    defaults\n",
      "zstd                      1.4.0                h3b9ef0a_0    conda-forge\n",
      "\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%%local\n",
    "conda list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<table>\n",
       "  <tr>\n",
       "    <th>Magic</th>\n",
       "    <th>Example</th>\n",
       "    <th>Explanation</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td>info</td>\n",
       "    <td>%%info</td>\n",
       "    <td>Outputs session information for the current Livy endpoint.</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td>cleanup</td>\n",
       "    <td>%%cleanup -f</td>\n",
       "    <td>Deletes all sessions for the current Livy endpoint, including this notebook's session. The force flag is mandatory.</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td>delete</td>\n",
       "    <td>%%delete -f -s 0</td>\n",
       "    <td>Deletes a session by number for the current Livy endpoint. Cannot delete this kernel's session.</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td>logs</td>\n",
       "    <td>%%logs</td>\n",
       "    <td>Outputs the current session's Livy logs.</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td>configure</td>\n",
       "    <td>%%configure -f<br/>{\"executorMemory\": \"1000M\", \"executorCores\": 4}</td>\n",
       "    <td>Configure the session creation parameters. The force flag is mandatory if a session has already been\n",
       "    created and the session will be dropped and recreated.<br/>Look at <a href=\"https://github.com/cloudera/livy#request-body\">\n",
       "    Livy's POST /sessions Request Body</a> for a list of valid parameters. Parameters must be passed in as a JSON string.</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td>spark</td>\n",
       "    <td>%%spark -o df<br/>df = spark.read.parquet('...</td>\n",
       "    <td>Executes spark commands.\n",
       "    Parameters:\n",
       "      <ul>\n",
       "        <li>-o VAR_NAME: The Spark dataframe of name VAR_NAME will be available in the %%local Python context as a\n",
       "          <a href=\"http://pandas.pydata.org/\">Pandas</a> dataframe with the same name.</li>\n",
       "        <li>-m METHOD: Sample method, either <tt>take</tt> or <tt>sample</tt>.</li>\n",
       "        <li>-n MAXROWS: The maximum number of rows of a dataframe that will be pulled from Livy to Jupyter.\n",
       "            If this number is negative, then the number of rows will be unlimited.</li>\n",
       "        <li>-r FRACTION: Fraction used for sampling.</li>\n",
       "      </ul>\n",
       "    </td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td>display</td>\n",
       "    <td>%%display -n 100<br/>my_spark_df</td>\n",
       "    <td>Displays Spark dataframe as a beautiful HTML table with horizontal and vertical scroll bars(if necessary).\n",
       "    Parameters:\n",
       "      <ul>\n",
       "        <li>-m METHOD: Sample method, either <tt>take</tt> or <tt>sample</tt>.</li>\n",
       "        <li>-n MAXROWS: The maximum number of rows of a dataframe to display.\n",
       "            If this number is negative, then the number of rows will be unlimited.</li>\n",
       "        <li>-r FRACTION: Fraction used for sampling.</li>\n",
       "      </ul>\n",
       "    </td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td>sql</td>\n",
       "    <td>%%sql -o tables -q<br/>SHOW TABLES</td>\n",
       "    <td>Executes a SQL query against the variable sqlContext (Spark v1.x) or spark (Spark v2.x).\n",
       "    Parameters:\n",
       "      <ul>\n",
       "        <li>-o VAR_NAME: The result of the SQL query will be available in the %%local Python context as a\n",
       "          <a href=\"http://pandas.pydata.org/\">Pandas</a> dataframe.</li>\n",
       "        <li>-q: The magic will return None instead of the dataframe (no visualization).</li>\n",
       "        <li>-m, -n, -r are the same as the %%spark parameters above.</li>\n",
       "      </ul>\n",
       "    </td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td>local</td>\n",
       "    <td>%%local<br/>a = 1</td>\n",
       "    <td>All the code in subsequent lines will be executed locally. Code must be valid Python code.</td>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%help"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "94ce942e34414873bd873f6211225555",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------+------------+------------+---------------+--------------------+---------------------+--------+------------+-------------+-----------+-------------+\n",
      "|VendorID|PULocationID|DOLocationID|passenger_count|lpep_pickup_datetime|lpep_dropoff_datetime|duration|total_amount|trip_distance|minute_rate|average_speed|\n",
      "+--------+------------+------------+---------------+--------------------+---------------------+--------+------------+-------------+-----------+-------------+\n",
      "|       2|          97|          49|              2| 2019-01-01 00:10:16|  2019-01-01 00:16:32|     376|         7.3|          .86|      1.165|        8.234|\n",
      "|       2|          49|         189|              2| 2019-01-01 00:27:11|  2019-01-01 00:31:38|     267|         5.8|          .66|      1.303|        8.899|\n",
      "|       2|         189|          17|              2| 2019-01-01 00:46:20|  2019-01-01 01:04:54|    1114|       19.71|         2.68|      1.062|        8.661|\n",
      "|       2|          82|         258|              1| 2019-01-01 00:19:06|  2019-01-01 00:39:43|    1237|        19.3|         4.53|      0.936|       13.184|\n",
      "|       2|          49|          17|              1| 2019-01-01 00:12:35|  2019-01-01 00:19:09|     394|         7.8|         1.05|      1.188|        9.594|\n",
      "|       2|         255|          33|              1| 2019-01-01 00:47:55|  2019-01-01 01:00:01|     726|        14.8|         3.77|      1.223|       18.694|\n",
      "|       1|          76|         225|              1| 2019-01-01 00:12:47|  2019-01-01 00:30:50|    1083|        17.3|         4.10|      0.958|       13.629|\n",
      "|       2|          25|          89|              1| 2019-01-01 00:16:23|  2019-01-01 00:39:46|    1403|        26.8|         7.75|      1.146|       19.886|\n",
      "|       2|          85|          39|              1| 2019-01-01 00:58:02|  2019-01-01 01:19:02|    1260|        16.8|         3.68|        0.8|       10.514|\n",
      "|       2|         223|         238|              1| 2019-01-01 00:37:00|  2019-01-01 00:56:42|    1182|       37.06|         6.84|      1.881|       20.832|\n",
      "|       2|         129|         129|              2| 2019-01-01 00:13:48|  2019-01-01 00:21:00|     432|        9.36|         1.15|        1.3|        9.583|\n",
      "|       2|          71|          71|              1| 2019-01-01 00:19:59|  2019-01-01 00:45:50|    1551|        16.8|          .49|       0.65|        1.137|\n",
      "|       2|          85|         177|              1| 2019-01-01 00:57:57|  2019-01-01 01:20:10|    1333|        18.3|         3.61|      0.824|        9.749|\n",
      "|       1|         256|          80|              1| 2019-01-01 00:09:02|  2019-01-01 00:17:50|     528|          14|         1.20|      1.591|        8.182|\n",
      "|       1|          80|          80|              1| 2019-01-01 00:22:12|  2019-01-01 00:25:29|     197|         5.3|          .50|      1.614|        9.137|\n",
      "|       1|         256|         229|              1| 2019-01-01 00:31:55|  2019-01-01 00:52:59|    1264|       24.95|         5.50|      1.184|       15.665|\n",
      "|       2|         255|         231|              1| 2019-01-01 00:30:20|  2019-01-01 00:54:19|    1439|       25.56|         5.01|      1.066|       12.534|\n",
      "|       2|         146|         129|              1| 2019-01-01 00:40:17|  2019-01-01 00:50:23|     606|       14.16|         2.72|      1.402|       16.158|\n",
      "|       2|         129|         157|              1| 2019-01-01 00:51:16|  2019-01-01 01:04:10|     774|       14.75|         2.15|      1.143|         10.0|\n",
      "|       2|         256|         112|              5| 2019-01-01 00:30:28|  2019-01-01 00:41:18|     650|       14.04|         2.18|      1.296|       12.074|\n",
      "+--------+------------+------------+---------------+--------------------+---------------------+--------+------------+-------------+-----------+-------------+\n",
      "only showing top 20 rows"
     ]
    }
   ],
   "source": [
    "tmp = df.select(['VendorID','PULocationID', 'DOLocationID', 'p_count', 'lpep_pickup_datetime', 'lpep_dropoff_datetime', 'duration','total_amount','trip_distance','minute_rate','average_speed'])\n",
    "tmp.show(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d56cccd0ee114d78a6579e7accf2943c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "version_minor": 0
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      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------------+-------+\n",
      "|passenger_count|  count|\n",
      "+---------------+-------+\n",
      "|              1|4705631|\n",
      "|              2| 406405|\n",
      "|           null| 304825|\n",
      "|              5| 161783|\n",
      "|              6|  86660|\n",
      "|              3|  75135|\n",
      "|              4|  28172|\n",
      "|              0|  10584|\n",
      "|              8|     58|\n",
      "|              7|     36|\n",
      "+---------------+-------+"
     ]
    }
   ],
   "source": [
    "df.groupBy('p_count').count().orderBy('count', ascending=False).limit(10).show(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+------+\n",
      "|PULocationID| count|\n",
      "+------------+------+\n",
      "|          74|457848|\n",
      "|          75|368752|\n",
      "|          41|333623|\n",
      "|          82|259303|\n",
      "|           7|244131|\n",
      "+------------+------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "toplo = df.groupBy('PULocationID').count().orderBy('count', ascending=False).limit(10)\n",
    "toplo.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+------------+-----+\n",
      "|PULocationID|DOLocationID|count|\n",
      "+------------+------------+-----+\n",
      "|          75|          74|72275|\n",
      "|           7|           7|67275|\n",
      "|          74|          75|61992|\n",
      "|          41|          42|56941|\n",
      "|          95|          95|52950|\n",
      "+------------+------------+-----+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "toplo2 = df.groupBy('PULocationID','DOLocationID').count().orderBy('count', ascending=False).limit(10)\n",
    "toplo2.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a2cde955a0fe4ba2a22c52261aad6c8a",
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       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+-------------+--------------------+------------+\n",
      "|LocationID|      Borough|                Zone|service_zone|\n",
      "+----------+-------------+--------------------+------------+\n",
      "|         1|          EWR|      Newark Airport|         EWR|\n",
      "|         2|       Queens|         Jamaica Bay|   Boro Zone|\n",
      "|         3|        Bronx|Allerton/Pelham G...|   Boro Zone|\n",
      "|         4|    Manhattan|       Alphabet City| Yellow Zone|\n",
      "|         5|Staten Island|       Arden Heights|   Boro Zone|\n",
      "+----------+-------------+--------------------+------------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "dfz.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
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       "model_id": "2133b54ff81d46b3a709bac654369ecb",
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       "VBox()"
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     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+------------+-----+------------+-----------------+------------+\n",
      "|PULocationID|DOLocationID|count|FromLocation|             Zone|service_zone|\n",
      "+------------+------------+-----+------------+-----------------+------------+\n",
      "|          75|          74|72275|   Manhattan|East Harlem South|   Boro Zone|\n",
      "|           7|           7|67275|      Queens|          Astoria|   Boro Zone|\n",
      "|          74|          75|61992|   Manhattan|East Harlem North|   Boro Zone|\n",
      "|          41|          42|56941|   Manhattan|   Central Harlem|   Boro Zone|\n",
      "|          95|          95|52950|      Queens|     Forest Hills|   Boro Zone|\n",
      "|          82|         129|49679|      Queens|         Elmhurst|   Boro Zone|\n",
      "|          41|          41|43924|   Manhattan|   Central Harlem|   Boro Zone|\n",
      "|          74|          41|43754|   Manhattan|East Harlem North|   Boro Zone|\n",
      "|          74|          74|43477|   Manhattan|East Harlem North|   Boro Zone|\n",
      "|         129|         129|43268|      Queens|  Jackson Heights|   Boro Zone|\n",
      "+------------+------------+-----+------------+-----------------+------------+"
     ]
    }
   ],
   "source": [
    "top_5_loc_names = toplo2.join(dfz, toplo2['PULocationID'].eqNullSafe(dfz['LocationID']),'left') \\\n",
    "            .withColumnRenamed('Borough','FromLocation') \\\n",
    "            .drop('LocationID') \\\n",
    "            .orderBy('count', ascending=False)\n",
    "top_5_loc_names.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<table>\n",
       "  <tr>\n",
       "    <th>Magic</th>\n",
       "    <th>Example</th>\n",
       "    <th>Explanation</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td>info</td>\n",
       "    <td>%%info</td>\n",
       "    <td>Outputs session information for the current Livy endpoint.</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td>cleanup</td>\n",
       "    <td>%%cleanup -f</td>\n",
       "    <td>Deletes all sessions for the current Livy endpoint, including this notebook's session. The force flag is mandatory.</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td>delete</td>\n",
       "    <td>%%delete -f -s 0</td>\n",
       "    <td>Deletes a session by number for the current Livy endpoint. Cannot delete this kernel's session.</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td>logs</td>\n",
       "    <td>%%logs</td>\n",
       "    <td>Outputs the current session's Livy logs.</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td>configure</td>\n",
       "    <td>%%configure -f<br/>{\"executorMemory\": \"1000M\", \"executorCores\": 4}</td>\n",
       "    <td>Configure the session creation parameters. The force flag is mandatory if a session has already been\n",
       "    created and the session will be dropped and recreated.<br/>Look at <a href=\"https://github.com/cloudera/livy#request-body\">\n",
       "    Livy's POST /sessions Request Body</a> for a list of valid parameters. Parameters must be passed in as a JSON string.</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td>spark</td>\n",
       "    <td>%%spark -o df<br/>df = spark.read.parquet('...</td>\n",
       "    <td>Executes spark commands.\n",
       "    Parameters:\n",
       "      <ul>\n",
       "        <li>-o VAR_NAME: The Spark dataframe of name VAR_NAME will be available in the %%local Python context as a\n",
       "          <a href=\"http://pandas.pydata.org/\">Pandas</a> dataframe with the same name.</li>\n",
       "        <li>-m METHOD: Sample method, either <tt>take</tt> or <tt>sample</tt>.</li>\n",
       "        <li>-n MAXROWS: The maximum number of rows of a dataframe that will be pulled from Livy to Jupyter.\n",
       "            If this number is negative, then the number of rows will be unlimited.</li>\n",
       "        <li>-r FRACTION: Fraction used for sampling.</li>\n",
       "      </ul>\n",
       "    </td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td>display</td>\n",
       "    <td>%%display -n 100<br/>my_spark_df</td>\n",
       "    <td>Displays Spark dataframe as a beautiful HTML table with horizontal and vertical scroll bars(if necessary).\n",
       "    Parameters:\n",
       "      <ul>\n",
       "        <li>-m METHOD: Sample method, either <tt>take</tt> or <tt>sample</tt>.</li>\n",
       "        <li>-n MAXROWS: The maximum number of rows of a dataframe to display.\n",
       "            If this number is negative, then the number of rows will be unlimited.</li>\n",
       "        <li>-r FRACTION: Fraction used for sampling.</li>\n",
       "      </ul>\n",
       "    </td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td>sql</td>\n",
       "    <td>%%sql -o tables -q<br/>SHOW TABLES</td>\n",
       "    <td>Executes a SQL query against the variable sqlContext (Spark v1.x) or spark (Spark v2.x).\n",
       "    Parameters:\n",
       "      <ul>\n",
       "        <li>-o VAR_NAME: The result of the SQL query will be available in the %%local Python context as a\n",
       "          <a href=\"http://pandas.pydata.org/\">Pandas</a> dataframe.</li>\n",
       "        <li>-q: The magic will return None instead of the dataframe (no visualization).</li>\n",
       "        <li>-m, -n, -r are the same as the %%spark parameters above.</li>\n",
       "      </ul>\n",
       "    </td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td>local</td>\n",
       "    <td>%%local<br/>a = 1</td>\n",
       "    <td>All the code in subsequent lines will be executed locally. Code must be valid Python code.</td>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%help"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
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      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
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    },
    {
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_5_loc_names.createOrReplaceTempView(\"top_5_loc_names_views\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
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     },
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    },
    {
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       "model_id": "23b79f0857b04d02a3fa6c1d968c6d99",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(HTML(value='Type:'), Button(description='Table', layout=Layout(width='70px'), st…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "621514ca62c1477b8d5aabc9b2477554",
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      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%sql -o top_5_loc_names -n -1\n",
    "select 'PULocationID','DOLocationID','count' from top_5_loc_names_views\n",
    "group by 'PULocationID','DOLocationID'\n",
    "ORDER BY count Desc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0f63d0e7a8f1441ab9c52822aaa9c1b1",
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       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]"
     ]
    }
   ],
   "source": [
    "import os\n",
    "all_files_in_cur_dir = os.listdir('.') \n",
    "csvfileslist = list(filter(lambda x:(x[-6:] == '.ipynb'),all_files_in_cur_dir))\n",
    "csvfileslist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "547ca044b11443178da8c745b5aaca5e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_minor": 0
      },
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['var', 'lib64', 'emr', 'home', 'lost+found', 'sbin', 'selinux', 'lib', 'srv', 'device-names.json', 'media', 'tmp', 'mnt', 'run', 'cgroup', 'bin', 'etc', 'dev', 'mnt1', '.autofsck', 'boot', 'opt', 'usr', 'proc', 'sys', 'root', 'local', '.autorelabel']"
     ]
    }
   ],
   "source": [
    "csvfileslist = list(os.listdir('.'))\n",
    "csvfileslist\n",
    "os.listdir('.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a1641c4f208a4781aba5d8a00a375c1a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
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       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['LoanStats3a.csv.zip', 'taxi_zones.zip']"
     ]
    }
   ],
   "source": [
    "tmpfile = list(filter(lambda x:(x[-4:] == '.zip'),os.listdir('/tmp')))\n",
    "tmpfile"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "import zipfile\n",
    "urllib.request.urlretrieve(\"https://s3.amazonaws.com/nyc-tlc/misc/taxi_zones.zip\", \"/tmp/taxi_zones.zip\")\n",
    "with zipfile.ZipFile(\"/tmp/taxi_zones.zip\",\"r\") as zip_ref:\n",
    "    zip_ref.extractall(\"/tmp/shape\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3a3a711686264327b6bde071a0aea34d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['taxi_zones.prj', 'taxi_zones.dbf', 'taxi_zones.shp', 'taxi_zones.shp.xml', 'taxi_zones.shx', 'taxi_zones.sbn', 'taxi_zones.sbx']"
     ]
    }
   ],
   "source": [
    "os.listdir('/tmp/shape')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
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   },
   "outputs": [
    {
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       "VBox()"
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     },
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    },
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      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#read in our raw dataset\n",
    "# shpt1 = sc.binaryFiles('s3://data-etl-o-original-raw/zone/taxi_zones_shape.zip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9e0699fadf074791a0f601920326591c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
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    },
    {
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    }
   ],
   "source": [
    "# shpt1.saveAsTextFile('/tmp/taxi_zones_shape_1.zip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
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   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "41f54faf42f04fb8a6cc7267d2a0809b",
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       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pandas\n",
      "  Using cached pandas-1.0.1-cp36-cp36m-manylinux1_x86_64.whl (10.1 MB)\n",
      "Collecting python-dateutil>=2.6.1\n",
      "  Using cached python_dateutil-2.8.1-py2.py3-none-any.whl (227 kB)\n",
      "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib64/python3.6/site-packages (from pandas) (1.14.5)\n",
      "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/site-packages (from pandas) (2019.3)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/site-packages (from python-dateutil>=2.6.1->pandas) (1.13.0)\n",
      "Installing collected packages: python-dateutil, pandas\n",
      "Successfully installed pandas-1.0.1 python-dateutil-2.8.1\n",
      "\n",
      "Processing /mnt/var/lib/livy/.cache/pip/wheels/1b/ab/b5/333342ac6838b5ab6977d3b5d29140cf3e60b01b220f8178c0/pyshp-2.1.0-py3-none-any.whl\n",
      "Installing collected packages: pyshp\n",
      "Successfully installed pyshp-2.1.0\n",
      "\n",
      "Collecting shapely\n",
      "  Using cached Shapely-1.7.0-cp36-cp36m-manylinux1_x86_64.whl (1.8 MB)\n",
      "Installing collected packages: shapely\n",
      "Successfully installed shapely-1.7.0\n",
      "\n",
      "Collecting descartes\n",
      "  Using cached descartes-1.1.0-py3-none-any.whl (5.8 kB)\n",
      "Collecting matplotlib\n",
      "  Using cached matplotlib-3.2.0-cp36-cp36m-manylinux1_x86_64.whl (12.4 MB)\n",
      "Collecting pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1\n",
      "  Using cached pyparsing-2.4.6-py2.py3-none-any.whl (67 kB)\n",
      "Requirement already satisfied: numpy>=1.11 in /usr/local/lib64/python3.6/site-packages (from matplotlib->descartes) (1.14.5)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /mnt/tmp/1584259980907-0/lib/python3.6/site-packages (from matplotlib->descartes) (2.8.1)\n",
      "Collecting kiwisolver>=1.0.1\n",
      "  Using cached kiwisolver-1.1.0-cp36-cp36m-manylinux1_x86_64.whl (90 kB)\n",
      "Collecting cycler>=0.10\n",
      "  Using cached cycler-0.10.0-py2.py3-none-any.whl (6.5 kB)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/site-packages (from python-dateutil>=2.1->matplotlib->descartes) (1.13.0)\n",
      "Requirement already satisfied: setuptools in /mnt/tmp/1584259980907-0/lib/python3.6/site-packages (from kiwisolver>=1.0.1->matplotlib->descartes) (46.0.0)\n",
      "Installing collected packages: pyparsing, kiwisolver, cycler, matplotlib, descartes\n",
      "Successfully installed cycler-0.10.0 descartes-1.1.0 kiwisolver-1.1.0 matplotlib-3.2.0 pyparsing-2.4.6"
     ]
    }
   ],
   "source": [
    "sc.install_pypi_package(\"pandas\")\n",
    "sc.install_pypi_package(\"pyshp\")\n",
    "sc.install_pypi_package(\"shapely\")\n",
    "sc.install_pypi_package(\"descartes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
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      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0818533ba1344c0985972e722fd776f6",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "VBox()"
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     },
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    },
    {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import urllib.request\n",
    "import zipfile\n",
    "import random\n",
    "import itertools\n",
    "import math\n",
    "import shapefile\n",
    "from shapely.geometry import Polygon\n",
    "from descartes.patch import PolygonPatch\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "src format:\n",
    "\n",
    "Record #0:[1, 0.116357453189, 0.0007823067885, 'Newark Airport', 1, 'EWR']\n",
    "\n",
    "target format\n",
    "\n",
    "{'OBJECTID': 1, 'Shape_Leng': 0.116357453189, 'Shape_Area': 0.0007823067885, 'zone': 'Newark Airport', 'LocationID': 1, 'borough': 'EWR'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
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   },
   "outputs": [
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      "text/plain": [
       "VBox()"
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     },
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    },
    {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "{'OBJECTID': 3, 'Shape_Leng': 0.0843411059012, 'Shape_Area': 0.000314414156821, 'zone': 'Allerton/Pelham Gardens', 'LocationID': 3, 'borough': 'Bronx'}\n",
      "{'OBJECTID': 4, 'Shape_Leng': 0.0435665270921, 'Shape_Area': 0.000111871946192, 'zone': 'Alphabet City', 'LocationID': 4, 'borough': 'Manhattan'}\n",
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      "{'OBJECTID': 6, 'Shape_Leng': 0.150490542523, 'Shape_Area': 0.000606460984581, 'zone': 'Arrochar/Fort Wadsworth', 'LocationID': 6, 'borough': 'Staten Island'}\n",
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      "{'OBJECTID': 10, 'Shape_Leng': 0.0998394794152, 'Shape_Area': 0.000435823818081, 'zone': 'Baisley Park', 'LocationID': 10, 'borough': 'Queens'}\n",
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      "{'OBJECTID': 207, 'Shape_Leng': 0.0283756831492, 'Shape_Area': 4.47230848441e-05, 'zone': 'Saint Michaels Cemetery/Woodside', 'LocationID': 207, 'borough': 'Queens'}\n",
      "{'OBJECTID': 208, 'Shape_Leng': 0.214721009581, 'Shape_Area': 0.00202032598969, 'zone': 'Schuylerville/Edgewater Park', 'LocationID': 208, 'borough': 'Bronx'}\n",
      "{'OBJECTID': 209, 'Shape_Leng': 0.0306953113473, 'Shape_Area': 3.88703351209e-05, 'zone': 'Seaport', 'LocationID': 209, 'borough': 'Manhattan'}\n",
      "{'OBJECTID': 210, 'Shape_Leng': 0.114851983372, 'Shape_Area': 0.000453155131253, 'zone': 'Sheepshead Bay', 'LocationID': 210, 'borough': 'Brooklyn'}\n",
      "{'OBJECTID': 211, 'Shape_Leng': 0.0252345082132, 'Shape_Area': 3.97291966087e-05, 'zone': 'SoHo', 'LocationID': 211, 'borough': 'Manhattan'}\n",
      "{'OBJECTID': 212, 'Shape_Leng': 0.0671290653429, 'Shape_Area': 0.000161276191858, 'zone': 'Soundview/Bruckner', 'LocationID': 212, 'borough': 'Bronx'}\n",
      "{'OBJECTID': 213, 'Shape_Leng': 0.159958783822, 'Shape_Area': 0.000904077906339, 'zone': 'Soundview/Castle Hill', 'LocationID': 213, 'borough': 'Bronx'}\n",
      "{'OBJECTID': 214, 'Shape_Leng': 0.135300314246, 'Shape_Area': 0.000811976523012, 'zone': 'South Beach/Dongan Hills', 'LocationID': 214, 'borough': 'Staten Island'}\n",
      "{'OBJECTID': 215, 'Shape_Leng': 0.103556453625, 'Shape_Area': 0.000395647022725, 'zone': 'South Jamaica', 'LocationID': 215, 'borough': 'Queens'}\n",
      "{'OBJECTID': 216, 'Shape_Leng': 0.116797554681, 'Shape_Area': 0.000816076647781, 'zone': 'South Ozone Park', 'LocationID': 216, 'borough': 'Queens'}\n",
      "{'OBJECTID': 217, 'Shape_Leng': 0.0553905251285, 'Shape_Area': 0.000114733270758, 'zone': 'South Williamsburg', 'LocationID': 217, 'borough': 'Brooklyn'}\n",
      "{'OBJECTID': 218, 'Shape_Leng': 0.0837008281049, 'Shape_Area': 0.000281293736407, 'zone': 'Springfield Gardens North', 'LocationID': 218, 'borough': 'Queens'}\n",
      "{'OBJECTID': 219, 'Shape_Leng': 0.155126669675, 'Shape_Area': 0.000428176336771, 'zone': 'Springfield Gardens South', 'LocationID': 219, 'borough': 'Queens'}\n",
      "{'OBJECTID': 220, 'Shape_Leng': 0.106015943267, 'Shape_Area': 0.000313035744467, 'zone': 'Spuyten Duyvil/Kingsbridge', 'LocationID': 220, 'borough': 'Bronx'}\n",
      "{'OBJECTID': 221, 'Shape_Leng': 0.166218220846, 'Shape_Area': 0.000890111567051, 'zone': 'Stapleton', 'LocationID': 221, 'borough': 'Staten Island'}\n",
      "{'OBJECTID': 222, 'Shape_Leng': 0.0483893527976, 'Shape_Area': 0.000130268343918, 'zone': 'Starrett City', 'LocationID': 222, 'borough': 'Brooklyn'}\n",
      "{'OBJECTID': 223, 'Shape_Leng': 0.166021925275, 'Shape_Area': 0.00076436070058, 'zone': 'Steinway', 'LocationID': 223, 'borough': 'Queens'}\n",
      "{'OBJECTID': 224, 'Shape_Leng': 0.0440204462769, 'Shape_Area': 9.84786989135e-05, 'zone': 'Stuy Town/Peter Cooper Village', 'LocationID': 224, 'borough': 'Manhattan'}\n",
      "{'OBJECTID': 225, 'Shape_Leng': 0.0887992073281, 'Shape_Area': 0.000310835395162, 'zone': 'Stuyvesant Heights', 'LocationID': 225, 'borough': 'Brooklyn'}\n",
      "{'OBJECTID': 226, 'Shape_Leng': 0.168957762041, 'Shape_Area': 0.00076194414504, 'zone': 'Sunnyside', 'LocationID': 226, 'borough': 'Queens'}\n",
      "{'OBJECTID': 227, 'Shape_Leng': 0.0828301570789, 'Shape_Area': 0.000268316529534, 'zone': 'Sunset Park East', 'LocationID': 227, 'borough': 'Brooklyn'}\n",
      "{'OBJECTID': 228, 'Shape_Leng': 0.177685177706, 'Shape_Area': 0.000993414783816, 'zone': 'Sunset Park West', 'LocationID': 228, 'borough': 'Brooklyn'}\n",
      "{'OBJECTID': 229, 'Shape_Leng': 0.0425252900116, 'Shape_Area': 9.47058230931e-05, 'zone': 'Sutton Place/Turtle Bay North', 'LocationID': 229, 'borough': 'Manhattan'}\n",
      "{'OBJECTID': 230, 'Shape_Leng': 0.0310283096779, 'Shape_Area': 5.60914463266e-05, 'zone': 'Times Sq/Theatre District', 'LocationID': 230, 'borough': 'Manhattan'}\n",
      "{'OBJECTID': 231, 'Shape_Leng': 0.0634201165587, 'Shape_Area': 0.000166929752992, 'zone': 'TriBeCa/Civic Center', 'LocationID': 231, 'borough': 'Manhattan'}\n",
      "{'OBJECTID': 232, 'Shape_Leng': 0.0614709085331, 'Shape_Area': 0.000216049973456, 'zone': 'Two Bridges/Seward Park', 'LocationID': 232, 'borough': 'Manhattan'}\n",
      "{'OBJECTID': 233, 'Shape_Leng': 0.048035918294, 'Shape_Area': 0.000116188094682, 'zone': 'UN/Turtle Bay South', 'LocationID': 233, 'borough': 'Manhattan'}\n",
      "{'OBJECTID': 234, 'Shape_Leng': 0.0360721994984, 'Shape_Area': 7.31054382894e-05, 'zone': 'Union Sq', 'LocationID': 234, 'borough': 'Manhattan'}\n",
      "{'OBJECTID': 235, 'Shape_Leng': 0.0761668426519, 'Shape_Area': 0.000212844547337, 'zone': 'University Heights/Morris Heights', 'LocationID': 235, 'borough': 'Bronx'}\n",
      "{'OBJECTID': 236, 'Shape_Leng': 0.0442519223099, 'Shape_Area': 0.000102864345211, 'zone': 'Upper East Side North', 'LocationID': 236, 'borough': 'Manhattan'}\n",
      "{'OBJECTID': 237, 'Shape_Leng': 0.0422126003388, 'Shape_Area': 9.59873206621e-05, 'zone': 'Upper East Side South', 'LocationID': 237, 'borough': 'Manhattan'}\n",
      "{'OBJECTID': 238, 'Shape_Leng': 0.0601093114033, 'Shape_Area': 0.000184763693765, 'zone': 'Upper West Side North', 'LocationID': 238, 'borough': 'Manhattan'}\n",
      "{'OBJECTID': 239, 'Shape_Leng': 0.0636261152958, 'Shape_Area': 0.000204715440774, 'zone': 'Upper West Side South', 'LocationID': 239, 'borough': 'Manhattan'}\n",
      "{'OBJECTID': 240, 'Shape_Leng': 0.146069764379, 'Shape_Area': 0.000722130920707, 'zone': 'Van Cortlandt Park', 'LocationID': 240, 'borough': 'Bronx'}\n",
      "{'OBJECTID': 241, 'Shape_Leng': 0.0687645777649, 'Shape_Area': 0.00025467697592, 'zone': 'Van Cortlandt Village', 'LocationID': 241, 'borough': 'Bronx'}\n",
      "{'OBJECTID': 242, 'Shape_Leng': 0.138136446433, 'Shape_Area': 0.000360072450014, 'zone': 'Van Nest/Morris Park', 'LocationID': 242, 'borough': 'Bronx'}\n",
      "{'OBJECTID': 243, 'Shape_Leng': 0.0943314999364, 'Shape_Area': 0.000438377341503, 'zone': 'Washington Heights North', 'LocationID': 243, 'borough': 'Manhattan'}\n",
      "{'OBJECTID': 244, 'Shape_Leng': 0.0805686044599, 'Shape_Area': 0.000359702747951, 'zone': 'Washington Heights South', 'LocationID': 244, 'borough': 'Manhattan'}\n",
      "{'OBJECTID': 245, 'Shape_Leng': 0.0959830596604, 'Shape_Area': 0.000466175414294, 'zone': 'West Brighton', 'LocationID': 245, 'borough': 'Staten Island'}\n",
      "{'OBJECTID': 246, 'Shape_Leng': 0.0694671937437, 'Shape_Area': 0.000281298327076, 'zone': 'West Chelsea/Hudson Yards', 'LocationID': 246, 'borough': 'Manhattan'}\n",
      "{'OBJECTID': 247, 'Shape_Leng': 0.0929678582271, 'Shape_Area': 0.000205617157843, 'zone': 'West Concourse', 'LocationID': 247, 'borough': 'Bronx'}\n",
      "{'OBJECTID': 248, 'Shape_Leng': 0.0569191167671, 'Shape_Area': 0.000149592769799, 'zone': 'West Farms/Bronx River', 'LocationID': 248, 'borough': 'Bronx'}\n",
      "{'OBJECTID': 249, 'Shape_Leng': 0.03638434365, 'Shape_Area': 7.22155957729e-05, 'zone': 'West Village', 'LocationID': 249, 'borough': 'Manhattan'}\n",
      "{'OBJECTID': 250, 'Shape_Leng': 0.0796263465454, 'Shape_Area': 0.000240975845956, 'zone': 'Westchester Village/Unionport', 'LocationID': 250, 'borough': 'Bronx'}\n",
      "{'OBJECTID': 251, 'Shape_Leng': 0.1377111611, 'Shape_Area': 0.000625754983157, 'zone': 'Westerleigh', 'LocationID': 251, 'borough': 'Staten Island'}\n",
      "{'OBJECTID': 252, 'Shape_Leng': 0.158004373564, 'Shape_Area': 0.00102526406057, 'zone': 'Whitestone', 'LocationID': 252, 'borough': 'Queens'}\n",
      "{'OBJECTID': 253, 'Shape_Leng': 0.0360514998192, 'Shape_Area': 7.83395761191e-05, 'zone': 'Willets Point', 'LocationID': 253, 'borough': 'Queens'}\n",
      "{'OBJECTID': 254, 'Shape_Leng': 0.0858863754861, 'Shape_Area': 0.000360040216032, 'zone': 'Williamsbridge/Olinville', 'LocationID': 254, 'borough': 'Bronx'}\n",
      "{'OBJECTID': 255, 'Shape_Leng': 0.0623841997664, 'Shape_Area': 0.000172309184842, 'zone': 'Williamsburg (North Side)', 'LocationID': 255, 'borough': 'Brooklyn'}\n",
      "{'OBJECTID': 256, 'Shape_Leng': 0.0679149669603, 'Shape_Area': 0.000168611097013, 'zone': 'Williamsburg (South Side)', 'LocationID': 256, 'borough': 'Brooklyn'}\n",
      "{'OBJECTID': 257, 'Shape_Leng': 0.0586690259793, 'Shape_Area': 0.00013890947321, 'zone': 'Windsor Terrace', 'LocationID': 257, 'borough': 'Brooklyn'}\n",
      "{'OBJECTID': 258, 'Shape_Leng': 0.0890133787693, 'Shape_Area': 0.000366209617143, 'zone': 'Woodhaven', 'LocationID': 258, 'borough': 'Queens'}\n",
      "{'OBJECTID': 259, 'Shape_Leng': 0.126750305191, 'Shape_Area': 0.000394552487366, 'zone': 'Woodlawn/Wakefield', 'LocationID': 259, 'borough': 'Bronx'}\n",
      "{'OBJECTID': 260, 'Shape_Leng': 0.133514154636, 'Shape_Area': 0.000422345326907, 'zone': 'Woodside', 'LocationID': 260, 'borough': 'Queens'}\n",
      "{'OBJECTID': 261, 'Shape_Leng': 0.0271204563616, 'Shape_Area': 3.43423231652e-05, 'zone': 'World Trade Center', 'LocationID': 261, 'borough': 'Manhattan'}\n",
      "{'OBJECTID': 262, 'Shape_Leng': 0.0490636231541, 'Shape_Area': 0.000122330270966, 'zone': 'Yorkville East', 'LocationID': 262, 'borough': 'Manhattan'}\n",
      "{'OBJECTID': 263, 'Shape_Leng': 0.0370166252994, 'Shape_Area': 6.57697664169e-05, 'zone': 'Yorkville West', 'LocationID': 263, 'borough': 'Manhattan'}"
     ]
    }
   ],
   "source": [
    "shp_attr = [dict(zip(fields_name, attr)) for attr in attributes]\n",
    "for sattr in shp_attr:\n",
    "    sattr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true,
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   "outputs": [
    {
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      "text/plain": [
       "VBox()"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_lat_lon(sf,shp_dic):\n",
    "    content = []\n",
    "    for sr in sf.shapeRecords():\n",
    "        shape = sr.shape\n",
    "        rec = sr.record\n",
    "        loc_id = rec[shp_dic['LocationID']]\n",
    "        x = (shape.bbox[0]+shape.bbox[2])/2\n",
    "        y = (shape.bbox[1]+shape.bbox[3])/2\n",
    "        content.append((loc_id, x, y))\n",
    "    return pd.DataFrame(content, columns=[\"LocationID\", \"longitude\", \"latitude\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sf = shapefile.Reader(\"/tmp/shape/taxi_zones.shp\")\n",
    "fields_name = [field[0] for field in sf.fields[1:]]\n",
    "shp_dic = dict(zip(fields_name, list(range(len(fields_name)))))\n",
    "attributes = sf.records()\n",
    "shp_attr = [dict(zip(fields_name, attr)) for attr in attributes]\n",
    "\n",
    "df_loc = pd.DataFrame(shp_attr).join(get_lat_lon(sf,shp_dic).set_index(\"LocationID\"), on=\"LocationID\")\n",
    "\n",
    "## df_loc.head()\n",
    "\n",
    "spark_dfl = spark.createDataFrame(df_loc)\n",
    "spark_dfl.show()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# draw zone map by plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true,
    "jupyter": {
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   "outputs": [
    {
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       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "version_minor": 0
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def draw_zone_map(ax, sf, heat={}, text=[], arrows=[]):\n",
    "    continent = [235/256, 151/256, 78/256]\n",
    "    ocean = (89/256, 171/256, 227/256)\n",
    "    theta = np.linspace(0, 2*np.pi, len(text)+1).tolist()\n",
    "    ax.set_facecolor(ocean)\n",
    "    \n",
    "    # colorbar\n",
    "    if len(heat) != 0:\n",
    "        norm = mpl.colors.Normalize(vmin=fmin(heat.values()),vmax=max(heat.values())) #norm = mpl.colors.LogNorm(vmin=1,vmax=max(heat))\n",
    "        cm=plt.get_cmap('Reds')\n",
    "        sm = plt.cm.ScalarMappable(cmap=cm, norm=norm)\n",
    "        sm.set_array([])\n",
    "        plt.colorbar(sm, ticks=np.linspace(min(heat.values()),max(heat.values()),8),\n",
    "                     boundaries=np.arange(min(heat.values())-10,max(heat.values())+10,.1))\n",
    "    \n",
    "    for sr in sf.shapeRecords():\n",
    "        shape = sr.shape\n",
    "        rec = sr.record\n",
    "        loc_id = rec[shp_dic['LocationID']]\n",
    "        zone = rec[shp_dic['zone']]\n",
    "        \n",
    "        if len(heat) == 0:\n",
    "            col = continent\n",
    "        else:\n",
    "            if loc_id not in heat:\n",
    "                R,G,B,A = cm(norm(0))\n",
    "            else:\n",
    "                R,G,B,A = cm(norm(heat[loc_id]))\n",
    "            col = [R,G,B]\n",
    "\n",
    "        # check number of parts (could use MultiPolygon class of shapely?)\n",
    "        nparts = len(shape.parts) # total parts\n",
    "        if nparts == 1:\n",
    "            polygon = Polygon(shape.points)\n",
    "            patch = PolygonPatch(polygon, facecolor=col, alpha=1.0, zorder=2)\n",
    "            ax.add_patch(patch)\n",
    "        else: # loop over parts of each shape, plot separately\n",
    "            for ip in range(nparts): # loop over parts, plot separately\n",
    "                i0 = shape.parts[ip]\n",
    "                if ip < nparts-1:\n",
    "                    i1 = shape.parts[ip+1]-1\n",
    "                else:\n",
    "                    i1 = len(shape.points)\n",
    "\n",
    "                polygon = Polygon(shape.points[i0:i1+1])\n",
    "                patch = PolygonPatch(polygon, facecolor=col, alpha=1.0, zorder=2)\n",
    "                ax.add_patch(patch)\n",
    "        \n",
    "        x = (shape.bbox[0]+shape.bbox[2])/2\n",
    "        y = (shape.bbox[1]+shape.bbox[3])/2\n",
    "        if (len(text) == 0 and rec[shp_dic['Shape_Area']] > 0.0001):\n",
    "            plt.text(x, y, str(loc_id), horizontalalignment='center', verticalalignment='center')            \n",
    "        elif len(text) != 0 and loc_id in text:\n",
    "            #plt.text(x+0.01, y-0.01, str(loc_id), fontsize=12, color=\"white\", bbox=dict(facecolor='black', alpha=0.5))\n",
    "            eta_x = 0.05*np.cos(theta[text.index(loc_id)])\n",
    "            eta_y = 0.05*np.sin(theta[text.index(loc_id)])\n",
    "            ax.annotate(\"[{}] {}\".format(loc_id, zone), xy=(x, y), xytext=(x+eta_x, y+eta_y),\n",
    "                        bbox=dict(facecolor='black', alpha=0.5), color=\"white\", fontsize=12,\n",
    "                        arrowprops=dict(facecolor='black', width=3, shrink=0.05))\n",
    "    if len(arrows)!=0:\n",
    "        for arr in arrows:\n",
    "            ax.annotate('', xy = arr['dest'], xytext = arr['src'], size = arr['cnt'],\n",
    "                    arrowprops=dict(arrowstyle=\"fancy\", fc=\"0.6\", ec=\"none\"))\n",
    "    \n",
    "    # display\n",
    "    limits = get_boundaries(sf)\n",
    "    plt.xlim(limits[0], limits[1])\n",
    "    plt.ylim(limits[2], limits[3])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# Draw Borough region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
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      "application/vnd.jupyter.widget-view+json": {
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       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "# %matplotlib inline\n",
    "\n",
    "def draw_region_map(ax, sf, heat={}):\n",
    "    continent = [235/256, 151/256, 78/256]\n",
    "    ocean = (89/256, 171/256, 227/256)    \n",
    "    \n",
    "    reg_list={'Staten Island':1, 'Queens':2, 'Bronx':3, 'Manhattan':4, 'EWR':5, 'Brooklyn':6}\n",
    "    reg_x = {'Staten Island':[], 'Queens':[], 'Bronx':[], 'Manhattan':[], 'EWR':[], 'Brooklyn':[]}\n",
    "    reg_y = {'Staten Island':[], 'Queens':[], 'Bronx':[], 'Manhattan':[], 'EWR':[], 'Brooklyn':[]}\n",
    "    \n",
    "    # colorbar\n",
    "    if len(heat) != 0:\n",
    "        norm = mpl.colors.Normalize(vmin=math.sqrt(min(heat.values())), vmax=math.sqrt(max(heat.values()))) #norm = mpl.colors.LogNorm(vmin=1,vmax=max(heat))\n",
    "        cm=plt.get_cmap('Reds')\n",
    "        #sm = plt.cm.ScalarMappable(cmap=cm, norm=norm)\n",
    "        #sm.set_array([])\n",
    "        #plt.colorbar(sm, ticks=np.linspace(min(heat.values()),max(heat.values()),8), \\\n",
    "        #             boundaries=np.arange(min(heat.values())-10,max(heat.values())+10,.1))\n",
    "    \n",
    "    ax.set_facecolor(ocean)\n",
    "    for sr in sf.shapeRecords():\n",
    "        shape = sr.shape\n",
    "        rec = sr.record\n",
    "        reg_name = rec[shp_dic['borough']]\n",
    "        \n",
    "        if len(heat) == 0:\n",
    "            norm = mpl.colors.Normalize(vmin=1,vmax=6) #norm = mpl.colors.LogNorm(vmin=1,vmax=max(heat))\n",
    "            cm=plt.get_cmap('Pastel1')\n",
    "            R,G,B,A = cm(norm(reg_list[reg_name]))\n",
    "            col = [R,G,B]\n",
    "        else:\n",
    "            R,G,B,A = cm(norm(math.sqrt(heat[reg_name])))\n",
    "            col = [R,G,B]\n",
    "            \n",
    "        # check number of parts (could use MultiPolygon class of shapely?)\n",
    "        nparts = len(shape.parts) # total parts\n",
    "        if nparts == 1:\n",
    "            polygon = Polygon(shape.points)\n",
    "            patch = PolygonPatch(polygon, facecolor=col, alpha=1.0, zorder=2)\n",
    "            ax.add_patch(patch)\n",
    "        else: # loop over parts of each shape, plot separately\n",
    "            for ip in range(nparts): # loop over parts, plot separately\n",
    "                i0 = shape.parts[ip]\n",
    "                if ip < nparts-1:\n",
    "                    i1 = shape.parts[ip+1]-1\n",
    "                else:\n",
    "                    i1 = len(shape.points)\n",
    "\n",
    "                polygon = Polygon(shape.points[i0:i1+1])\n",
    "                patch = PolygonPatch(polygon, facecolor=col, alpha=1.0, zorder=2)\n",
    "                ax.add_patch(patch)\n",
    "                \n",
    "        reg_x[reg_name].append((shape.bbox[0]+shape.bbox[2])/2)\n",
    "        reg_y[reg_name].append((shape.bbox[1]+shape.bbox[3])/2)\n",
    "        \n",
    "    for k in reg_list:\n",
    "        if len(heat)==0:\n",
    "            plt.text(np.mean(reg_x[k]), np.mean(reg_y[k]), k, horizontalalignment='center', verticalalignment='center',\n",
    "                        bbox=dict(facecolor='black', alpha=0.5), color=\"white\", fontsize=12)     \n",
    "        else:\n",
    "            plt.text(np.mean(reg_x[k]), np.mean(reg_y[k]), \"{}\\n({}K)\".format(k, heat[k]/1000), horizontalalignment='center', \n",
    "                     verticalalignment='center',bbox=dict(facecolor='black', alpha=0.5), color=\"white\", fontsize=12)       \n",
    "\n",
    "    # display\n",
    "    limits = get_boundaries(sf)\n",
    "    plt.xlim(limits[0], limits[1])\n",
    "    plt.ylim(limits[2], limits[3])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# get boundaries of zone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "501d4196d5d448ad8581dedcbb9cbe7c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %matplotlib inline\n",
    "\n",
    "def get_boundaries(sf):\n",
    "    lat, lon = [], []\n",
    "    for shape in list(sf.iterShapes()):\n",
    "        lat.extend([shape.bbox[0], shape.bbox[2]])\n",
    "        lon.extend([shape.bbox[1], shape.bbox[3]])\n",
    "\n",
    "    margin = 0.01 # buffer to add to the range\n",
    "    lat_min = min(lat) - margin\n",
    "    lat_max = max(lat) + margin\n",
    "    lon_min = min(lon) - margin\n",
    "    lon_max = max(lon) + margin\n",
    "\n",
    "    return lat_min, lat_max, lon_min, lon_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d2efe2498ebf49c8a057f3f732d72bff",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(15,8))\n",
    "ax = plt.subplot(1, 2, 1)\n",
    "ax.set_title(\"Borough Area in NYC\")\n",
    "draw_region_map(ax, sf)\n",
    "\n",
    "ax = plt.subplot(1, 2, 2)\n",
    "ax.set_title(\"Zones in NYC\")\n",
    "draw_zone_map(ax, sf)\n",
    "\n",
    "%matplot plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2445d940cb5a47b9a8984f5bfdf2c797",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------+------------+------------+---------------+--------------------+---------------------+--------+------------+-------------+-----------+-------------+\n",
      "|VendorID|PULocationID|DOLocationID|passenger_count|lpep_pickup_datetime|lpep_dropoff_datetime|duration|total_amount|trip_distance|minute_rate|average_speed|\n",
      "+--------+------------+------------+---------------+--------------------+---------------------+--------+------------+-------------+-----------+-------------+\n",
      "|       2|          97|          49|              2| 2019-01-01 00:10:16|  2019-01-01 00:16:32|     376|         7.3|          .86|     1.1649|        8.234|\n",
      "|       2|          49|         189|              2| 2019-01-01 00:27:11|  2019-01-01 00:31:38|     267|         5.8|          .66|     1.3034|       8.8989|\n",
      "|       2|         189|          17|              2| 2019-01-01 00:46:20|  2019-01-01 01:04:54|    1114|       19.71|         2.68|     1.0616|       8.6607|\n",
      "|       2|          82|         258|              1| 2019-01-01 00:19:06|  2019-01-01 00:39:43|    1237|        19.3|         4.53|     0.9361|      13.1835|\n",
      "|       2|          49|          17|              1| 2019-01-01 00:12:35|  2019-01-01 00:19:09|     394|         7.8|         1.05|     1.1878|       9.5939|\n",
      "|       2|         255|          33|              1| 2019-01-01 00:47:55|  2019-01-01 01:00:01|     726|        14.8|         3.77|     1.2231|      18.6942|\n",
      "|       1|          76|         225|              1| 2019-01-01 00:12:47|  2019-01-01 00:30:50|    1083|        17.3|         4.10|     0.9584|      13.6288|\n",
      "|       2|          25|          89|              1| 2019-01-01 00:16:23|  2019-01-01 00:39:46|    1403|        26.8|         7.75|     1.1461|       19.886|\n",
      "|       2|          85|          39|              1| 2019-01-01 00:58:02|  2019-01-01 01:19:02|    1260|        16.8|         3.68|        0.8|      10.5143|\n",
      "|       2|         223|         238|              1| 2019-01-01 00:37:00|  2019-01-01 00:56:42|    1182|       37.06|         6.84|     1.8812|      20.8325|\n",
      "+--------+------------+------------+---------------+--------------------+---------------------+--------+------------+-------------+-----------+-------------+\n",
      "only showing top 10 rows"
     ]
    }
   ],
   "source": [
    "df.select(['VendorID','PULocationID', 'DOLocationID', 'p_count', 'lpep_pickup_datetime', 'lpep_dropoff_datetime', 'duration','total_amount','trip_distance','minute_rate','average_speed']) \\\n",
    ".show(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f3e9560b285f4d46a1814470a322eb23",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------+--------------------+---------------------+----------+------------+------------+-------+-------------+-----------+-----+-------+----------+------------+---------------------+------------+------------+---------+--------+-----------+-------------+\n",
      "|VendorID|lpep_pickup_datetime|lpep_dropoff_datetime|RatecodeID|PULocationID|DOLocationID|p_count|trip_distance|fare_amount|extra|mta_tax|tip_amount|tolls_amount|improvement_surcharge|total_amount|payment_type|trip_type|duration|minute_rate|average_speed|\n",
      "+--------+--------------------+---------------------+----------+------------+------------+-------+-------------+-----------+-----+-------+----------+------------+---------------------+------------+------------+---------+--------+-----------+-------------+\n",
      "|       2| 2019-01-01 00:10:16|  2019-01-01 00:16:32|         1|          97|          49|      2|          .86|          6|  0.5|    0.5|         0|           0|                  0.3|         7.3|           2|        1|     376|     1.1649|        8.234|\n",
      "|       2| 2019-01-01 00:27:11|  2019-01-01 00:31:38|         1|          49|         189|      2|          .66|        4.5|  0.5|    0.5|         0|           0|                  0.3|         5.8|           1|        1|     267|     1.3034|       8.8989|\n",
      "|       2| 2019-01-01 00:46:20|  2019-01-01 01:04:54|         1|         189|          17|      2|         2.68|       13.5|  0.5|    0.5|      2.96|           0|                  0.3|       19.71|           1|        1|    1114|     1.0616|       8.6607|\n",
      "|       2| 2019-01-01 00:19:06|  2019-01-01 00:39:43|         1|          82|         258|      1|         4.53|         18|  0.5|    0.5|         0|           0|                  0.3|        19.3|           2|        1|    1237|     0.9361|      13.1835|\n",
      "|       2| 2019-01-01 00:12:35|  2019-01-01 00:19:09|         1|          49|          17|      1|         1.05|        6.5|  0.5|    0.5|         0|           0|                  0.3|         7.8|           1|        1|     394|     1.1878|       9.5939|\n",
      "|       2| 2019-01-01 00:47:55|  2019-01-01 01:00:01|         1|         255|          33|      1|         3.77|       13.5|  0.5|    0.5|         0|           0|                  0.3|        14.8|           1|        1|     726|     1.2231|      18.6942|\n",
      "|       1| 2019-01-01 00:12:47|  2019-01-01 00:30:50|         1|          76|         225|      1|         4.10|         16|  0.5|    0.5|         0|           0|                  0.3|        17.3|           1|        1|    1083|     0.9584|      13.6288|\n",
      "|       2| 2019-01-01 00:16:23|  2019-01-01 00:39:46|         1|          25|          89|      1|         7.75|       25.5|  0.5|    0.5|         0|           0|                  0.3|        26.8|           1|        1|    1403|     1.1461|       19.886|\n",
      "|       2| 2019-01-01 00:58:02|  2019-01-01 01:19:02|         1|          85|          39|      1|         3.68|       15.5|  0.5|    0.5|         0|           0|                  0.3|        16.8|           1|        1|    1260|        0.8|      10.5143|\n",
      "|       2| 2019-01-01 00:37:00|  2019-01-01 00:56:42|         1|         223|         238|      1|         6.84|         22|  0.5|    0.5|         8|        5.76|                  0.3|       37.06|           1|        1|    1182|     1.8812|      20.8325|\n",
      "+--------+--------------------+---------------------+----------+------------+------------+-------+-------------+-----------+-----+-------+----------+------------+---------------------+------------+------------+---------+--------+-----------+-------------+\n",
      "only showing top 10 rows"
     ]
    }
   ],
   "source": [
    "# rename passenger_count to pcount shortly\n",
    "df = df.withColumnRenamed('passenger_count', 'p_count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b0df100857ba488a8658bac65c43a276",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------+------------+------------+-------+--------------------+---------------------+--------+------------+-------------+-----------+-------------+\n",
      "|VendorID|PULocationID|DOLocationID|p_count|lpep_pickup_datetime|lpep_dropoff_datetime|duration|total_amount|trip_distance|minute_rate|average_speed|\n",
      "+--------+------------+------------+-------+--------------------+---------------------+--------+------------+-------------+-----------+-------------+\n",
      "|       2|          97|          49|      2| 2019-01-01 00:10:16|  2019-01-01 00:16:32|     376|         7.3|          .86|     1.1649|        8.234|\n",
      "|       2|          49|         189|      2| 2019-01-01 00:27:11|  2019-01-01 00:31:38|     267|         5.8|          .66|     1.3034|       8.8989|\n",
      "|       2|         189|          17|      2| 2019-01-01 00:46:20|  2019-01-01 01:04:54|    1114|       19.71|         2.68|     1.0616|       8.6607|\n",
      "|       2|          82|         258|      1| 2019-01-01 00:19:06|  2019-01-01 00:39:43|    1237|        19.3|         4.53|     0.9361|      13.1835|\n",
      "|       2|          49|          17|      1| 2019-01-01 00:12:35|  2019-01-01 00:19:09|     394|         7.8|         1.05|     1.1878|       9.5939|\n",
      "|       2|         255|          33|      1| 2019-01-01 00:47:55|  2019-01-01 01:00:01|     726|        14.8|         3.77|     1.2231|      18.6942|\n",
      "|       1|          76|         225|      1| 2019-01-01 00:12:47|  2019-01-01 00:30:50|    1083|        17.3|         4.10|     0.9584|      13.6288|\n",
      "|       2|          25|          89|      1| 2019-01-01 00:16:23|  2019-01-01 00:39:46|    1403|        26.8|         7.75|     1.1461|       19.886|\n",
      "|       2|          85|          39|      1| 2019-01-01 00:58:02|  2019-01-01 01:19:02|    1260|        16.8|         3.68|        0.8|      10.5143|\n",
      "|       2|         223|         238|      1| 2019-01-01 00:37:00|  2019-01-01 00:56:42|    1182|       37.06|         6.84|     1.8812|      20.8325|\n",
      "+--------+------------+------------+-------+--------------------+---------------------+--------+------------+-------------+-----------+-------------+\n",
      "only showing top 10 rows"
     ]
    }
   ],
   "source": [
    "\n",
    "df.select(['VendorID','PULocationID', 'DOLocationID', 'p_count', 'lpep_pickup_datetime', 'lpep_dropoff_datetime', 'duration','total_amount','trip_distance','minute_rate','average_speed']) \\\n",
    ".show(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "41b112eddccd400fb454af558e137808",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------+---------------+----------------+--------------------+----------+-------------+------------------+------------------+\n",
      "|OBJECTID|     Shape_Leng|      Shape_Area|                zone|LocationID|      borough|         longitude|          latitude|\n",
      "+--------+---------------+----------------+--------------------+----------+-------------+------------------+------------------+\n",
      "|       1| 0.116357453189|  7.823067885E-4|      Newark Airport|         1|          EWR| 936681.7498053049|190522.13027768553|\n",
      "|       2|  0.43346966679|0.00486634037837|         Jamaica Bay|         2|       Queens|1033536.0827942044|161853.98229982704|\n",
      "|       3|0.0843411059012|3.14414156821E-4|Allerton/Pelham G...|         3|        Bronx|  1027136.41219455|254730.01084850729|\n",
      "|       4|0.0435665270921|1.11871946192E-4|       Alphabet City|         4|    Manhattan| 990424.0195702165|203100.04043184966|\n",
      "|       5|0.0921464898574|4.97957489363E-4|       Arden Heights|         5|Staten Island| 932133.2098496482|139954.54193567485|\n",
      "|       6| 0.150490542523|6.06460984581E-4|Arrochar/Fort Wad...|         6|Staten Island| 964099.0173973292|157533.67206276953|\n",
      "|       7| 0.107417171123|3.89787989274E-4|             Astoria|         7|       Queens|1006324.9388191998|216569.41102095693|\n",
      "|       8|0.0275906911574| 2.6587716279E-5|        Astoria Park|         8|       Queens|1005541.5825920925| 222960.5823142156|\n",
      "|       9|0.0997840924705|3.38443803197E-4|          Auburndale|         9|       Queens|1043403.6924028024| 214170.7026207447|\n",
      "|      10|0.0998394794152|4.35823818081E-4|        Baisley Park|        10|       Queens|1042775.8015680313|186452.53824371845|\n",
      "|      11|0.0792110389596| 2.6452053504E-4|          Bath Beach|        11|     Brooklyn| 982164.4980031773|159335.25489003956|\n",
      "|      12|0.0366613013579|4.15116236727E-5|        Battery Park|        12|    Manhattan|  979889.680921644|195215.44749031216|\n",
      "|      13|0.0502813228631|1.49358592917E-4|   Battery Park City|        13|    Manhattan| 979844.2413037196|198508.15931824595|\n",
      "|      14| 0.175213698053|0.00138177826442|           Bay Ridge|        14|     Brooklyn| 976332.8627958819|166469.26262110472|\n",
      "|      15|  0.14433622262|9.25219395547E-4|Bay Terrace/Fort ...|        15|       Queens|1043719.6651142985|225304.68907873333|\n",
      "|      16| 0.141291873771|8.71889446182E-4|             Bayside|        16|       Queens|1046653.0270440206|216690.18732249737|\n",
      "|      17| 0.093522632948|3.22957654799E-4|             Bedford|        17|     Brooklyn| 997925.4938743934|191314.48353823274|\n",
      "|      18|0.0697995498569|1.48850163948E-4|        Bedford Park|        18|        Bronx|1014322.5144993141|255779.45435959846|\n",
      "|      19| 0.101824875452|5.46661094782E-4|           Bellerose|        19|       Queens|1059467.4358350784|207808.57245142013|\n",
      "|      20|0.0514401924362|1.34512633032E-4|             Belmont|        20|        Bronx|1015730.9563582167|251796.60542875528|\n",
      "+--------+---------------+----------------+--------------------+----------+-------------+------------------+------------------+\n",
      "only showing top 20 rows"
     ]
    }
   ],
   "source": [
    "spark_dfl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "28b92875a03245788478f6d2a587c1c7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+-------------+--------------------+------------+\n",
      "|LocationID|      Borough|                Zone|service_zone|\n",
      "+----------+-------------+--------------------+------------+\n",
      "|         1|          EWR|      Newark Airport|         EWR|\n",
      "|         2|       Queens|         Jamaica Bay|   Boro Zone|\n",
      "|         3|        Bronx|Allerton/Pelham G...|   Boro Zone|\n",
      "|         4|    Manhattan|       Alphabet City| Yellow Zone|\n",
      "|         5|Staten Island|       Arden Heights|   Boro Zone|\n",
      "|         6|Staten Island|Arrochar/Fort Wad...|   Boro Zone|\n",
      "|         7|       Queens|             Astoria|   Boro Zone|\n",
      "|         8|       Queens|        Astoria Park|   Boro Zone|\n",
      "|         9|       Queens|          Auburndale|   Boro Zone|\n",
      "|        10|       Queens|        Baisley Park|   Boro Zone|\n",
      "+----------+-------------+--------------------+------------+\n",
      "only showing top 10 rows"
     ]
    }
   ],
   "source": [
    "dfz.show(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3fb5c5942fa640b89e336f79ecf5efa4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(['LocationID', 'Borough', 'Zone', 'service_zone'], ['VendorID', 'lpep_pickup_datetime', 'lpep_dropoff_datetime', 'RatecodeID', 'PULocationID', 'DOLocationID', 'p_count', 'trip_distance', 'fare_amount', 'extra', 'mta_tax', 'tip_amount', 'tolls_amount', 'improvement_surcharge', 'total_amount', 'payment_type', 'trip_type', 'duration', 'minute_rate', 'average_speed'], ['OBJECTID', 'Shape_Leng', 'Shape_Area', 'zone', 'LocationID', 'borough', 'longitude', 'latitude'])"
     ]
    }
   ],
   "source": [
    "dfz.columns,df.columns,spark_dfl.columns"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "dfjoin1 = df.join(dfz, df['PULocationID'].eqNullSafe(dfz['LocationID']),'left') \\\n",
    "            .withColumnRenamed('Borough','FromBorough').withColumnRenamed('Zone','FromZone') \\\n",
    "            .drop('LocationID').drop('service_zone') \\\n",
    "            .orderBy('count', ascending=False)\n",
    "dfjoin1.show(10)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# for business trend to \n",
    "Q1: Which zone have most pickups and drop-offs?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pu = pd.read_sql_query('SELECT PULocationID AS LocationID, count(*) AS PUcount \\\n",
    "                        FROM table_record \\\n",
    "                        GROUP BY PULocationID', nyc_database)\n",
    "df_do = pd.read_sql_query('SELECT DOLocationID AS LocationID, count(*) AS DOcount \\\n",
    "                        FROM table_record \\\n",
    "                        GROUP BY DOLocationID', nyc_database)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "456021fac0094bffa1ebc0fa9f78e633",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['VendorID', 'lpep_pickup_datetime', 'lpep_dropoff_datetime', 'RatecodeID', 'PULocationID', 'DOLocationID', 'p_count', 'trip_distance', 'fare_amount', 'extra', 'mta_tax', 'tip_amount', 'tolls_amount', 'improvement_surcharge', 'total_amount', 'payment_type', 'trip_type', 'duration', 'minute_rate', 'average_speed']"
     ]
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "toplo = df.groupBy('PULocationID').count().orderBy('count', ascending=False).limit(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b846cd380a6c480c884551e42f699564",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## count().orderBy('count', ascending=False).limit(10)\n",
    "\n",
    "df_pu = df.select(f.col(\"PULocationID\").alias(\"LocationID\")).groupby('LocationID').count().withColumnRenamed('count', 'pu_count')\n",
    "df_do = df.select(f.col(\"DOLocationID\").alias(\"LocationID\")).groupby('LocationID').count().withColumnRenamed('count', 'do_count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "79e5cc73155d40ecbfc67c5f6071762c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+--------+\n",
      "|LocationID|pu_count|\n",
      "+----------+--------+\n",
      "|       125|      77|\n",
      "|         7|  244131|\n",
      "+----------+--------+\n",
      "only showing top 2 rows"
     ]
    }
   ],
   "source": [
    "df_pu.show(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fbeb2e45fb1b49f7a899e09ed140d2c1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+--------+\n",
      "|LocationID|do_count|\n",
      "+----------+--------+\n",
      "|       125|    3586|\n",
      "|         7|  150145|\n",
      "+----------+--------+\n",
      "only showing top 2 rows"
     ]
    }
   ],
   "source": [
    "df_do.show(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "118db57455564623a280efba40935efc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      },
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       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+--------+--------+-----------+\n",
      "|LocationID|pu_count|do_count|total_count|\n",
      "+----------+--------+--------+-----------+\n",
      "|       125|      77|    3586|       3663|\n",
      "|       124|    2441|    3898|       6339|\n",
      "|        51|   15028|   14029|      29057|\n",
      "|         7|  244131|  150145|     394276|\n",
      "|       169|   10191|   14368|      24559|\n",
      "+----------+--------+--------+-----------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "## use taxi zone talbe\n",
    "joined1 = df_pu.join(df_do, 'LocationID', 'left').withColumn('total_count', df_pu.pu_count + df_do.do_count)\n",
    "\n",
    "joined1.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
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      "application/vnd.jupyter.widget-view+json": {
       "model_id": "91b61fd7d52644c4af4cca9fad5cdb2f",
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       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+-------------+--------------------+------------+\n",
      "|LocationID|      Borough|                Zone|service_zone|\n",
      "+----------+-------------+--------------------+------------+\n",
      "|         1|          EWR|      Newark Airport|         EWR|\n",
      "|         2|       Queens|         Jamaica Bay|   Boro Zone|\n",
      "|         3|        Bronx|Allerton/Pelham G...|   Boro Zone|\n",
      "|         4|    Manhattan|       Alphabet City| Yellow Zone|\n",
      "|         5|Staten Island|       Arden Heights|   Boro Zone|\n",
      "+----------+-------------+--------------------+------------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "dfz.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "21c2a560fb7b44ee9d9d0483cf4fe50e",
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       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+--------+--------+-----------+---------+--------------------+------------+\n",
      "|LocationID|pu_count|do_count|total_count|  Borough|                Zone|service_zone|\n",
      "+----------+--------+--------+-----------+---------+--------------------+------------+\n",
      "|       125|      77|    3586|       3663|Manhattan|           Hudson Sq| Yellow Zone|\n",
      "|       124|    2441|    3898|       6339|   Queens|        Howard Beach|   Boro Zone|\n",
      "|        51|   15028|   14029|      29057|    Bronx|          Co-Op City|   Boro Zone|\n",
      "|         7|  244131|  150145|     394276|   Queens|             Astoria|   Boro Zone|\n",
      "|       169|   10191|   14368|      24559|    Bronx|          Mount Hope|   Boro Zone|\n",
      "|       205|   11476|   18682|      30158|   Queens|        Saint Albans|   Boro Zone|\n",
      "|        15|    1576|    2723|       4299|   Queens|Bay Terrace/Fort ...|   Boro Zone|\n",
      "|       232|     476|    8926|       9402|Manhattan|Two Bridges/Sewar...| Yellow Zone|\n",
      "|       234|     254|   13139|      13393|Manhattan|            Union Sq| Yellow Zone|\n",
      "|        54|    1891|   11563|      13454| Brooklyn|     Columbia Street|   Boro Zone|\n",
      "+----------+--------+--------+-----------+---------+--------------------+------------+\n",
      "only showing top 10 rows"
     ]
    }
   ],
   "source": [
    "joined2 = joined1.join(dfz,'LocationID', 'left')\n",
    "\n",
    "joined2.show(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_putp5 = joined2.select(['pu_count','zone','borough']).orderby('pu_count', ascending=False).limit(5)\n",
    "\n",
    "df_putp5.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      },
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       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+--------+--------+-----------+---------+--------------------+------------+\n",
      "|LocationID|pu_count|do_count|total_count|  Borough|                Zone|service_zone|\n",
      "+----------+--------+--------+-----------+---------+--------------------+------------+\n",
      "|       125|      77|    3586|       3663|Manhattan|           Hudson Sq| Yellow Zone|\n",
      "|       169|   10191|   14368|      24559|    Bronx|          Mount Hope|   Boro Zone|\n",
      "|       205|   11476|   18682|      30158|   Queens|        Saint Albans|   Boro Zone|\n",
      "|       132|     558|   29615|      30173|   Queens|         JFK Airport|    Airports|\n",
      "|       154|     307|     844|       1151| Brooklyn|Marine Park/Floyd...|   Boro Zone|\n",
      "|       101|    1701|    2358|       4059|   Queens|           Glen Oaks|   Boro Zone|\n",
      "|        11|    3151|    3681|       6832| Brooklyn|          Bath Beach|   Boro Zone|\n",
      "|        29|    7940|    7523|      15463| Brooklyn|      Brighton Beach|   Boro Zone|\n",
      "|        69|   22308|   28900|      51208|    Bronx|East Concourse/Co...|   Boro Zone|\n",
      "|        73|    1311|    4490|       5801|   Queens|       East Flushing|   Boro Zone|\n",
      "+----------+--------+--------+-----------+---------+--------------------+------------+"
     ]
    }
   ],
   "source": [
    "joined2.select('*').limit(10).show(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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